Top Data Analytics Course in Bangalore With Placement | Updated 2025

Data Analytics Course for all graduates, non-IT, Diploma & career gaps — ₹18,500/- only.

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Data Analytics Course in Bangalore

  • Join in Our Best Data Analytics Training Institute in Bangalore to Build Practical Data Skills.
  • Complete Data Analytics Training in Bangalore – Covers Excel, SQL, Python and Power BI.
  • Gain Real-world Experience Through Live Projects and Interactive Practical Sessions.
  • Enjoy Flexible Scheduling Options – Choose Weekday, Weekend, or Fast-track Batches.
  • Career-focused Data Analytics Certification Course With Guaranteed Placement Support
  • Receive Expert Guidance on Resume Building, Technical Interview and Career Planning.

WANT IT JOB

Become a Data Analyst in 3 Months

Freshers Salary

3 LPA

To

8 LPA

Quality Training With Affordable Fees in Bangalore!
INR ₹18000
INR ₹14000

10598+

(Placed)
Freshers To IT

5768+

(Placed)
Non IT To IT

7546+

(Placed)
Career Gap

3798+

(Placed)
Less Then 60%

Our Hiring Partners

Overview of Data Analytics Course in Bangalore

The Data Analytics Course provides a thorough learning path covering topics like data handling, analysis methods, and visualization tools. For those looking for a data analytics internship or who want to get a recognized Data Analytics Certification in this course is perfect. With hands-on training and expert guidance, learners are well-prepared for roles such as Business Analytics are provided by Data Analytics Placement. Gain practical experience through real-time Data Analytics Projects in Bangalore and build a strong foundation in data-driven decision-making.

What You'll Learn From Data Analytics Training

Master core Data Analytics tools and techniques, including Python, Excel, SQL and Power BI to build strong analytical foundations.

Explore key concepts such as data wrangling, data visualization, statistical analysis and predictive modeling in a real-world context.

Apply your learning through practical tasks and role-based simulations aligned with Data Analytics Internship opportunities to gain real world exposure.

Gain industry-relevant experience through hands-on projects business case studies and advanced analytics workflows.

Advance from beginner-level concepts to expert-level strategies that support impactful data-driven decision-making.

Enroll in the Data Analytics Course in Bangalore and earn a certification that boost your career prospects with personalized guidance and expert-led training.

Additional Info

Course Highlights

  • Start your learning journey by choosing from Excel, SQL, Python, Power BI, or Tableau – all included in one complete Data Analytics course.
  • Get full job support with top companies looking for skilled data analysts.
  • Join 11,000+ students who have been trained and placed through our 350+ hiring partners.
  • Learn from expert trainers with over 10 years of real work experience in the industry.
  • This course includes easy lessons practical projects and full career support.
  • With low fees flexible timings and placement help, it is a great choice for beginners.
  • Build real world skills and boost your career in Data Analytics with hands-on training.

Exploring the Benefits of Data Analytics Course

  • High Demand in Every Industry – Data skills are needed in almost all fields like banking, marketing, and e-commerce. This course prepares you to work in different sectors with strong data knowledge. The demand for data experts keeps growing every year. This means more chances for you to find stable and rewarding jobs.
  • Build Confidence with Numbers – You will learn how to work with numbers and make sense of them easily. This builds your confidence in dealing with reports and business information. Even if you're not good at math, the course helps you understand it better. It makes you feel more comfortable working in data-focused roles.
  • Make Your Resume Stand Out – Completing this Data Analytics Course adds value to your resume with a job ready skill set. Recruiters are impressed by candidates who can work with data. It shows that you are serious about your career and future. Having data skills gives you an edge over other applicants.
  • Understand How Businesses Work – By learning data analytics, you see how companies make decisions using facts. You’ll understand what drives sales, customer behavior, or performance. This helps you take part in planning and discussions at work. It makes you more involved and important to your team.
  • Learn from Industry Experts – The course is taught by trainers who’ve worked with real companies and data. You get useful tips and knowledge based on actual experience. They guide you step by step so you won’t feel lost. Learning from experts makes your journey easier and more meaningful.

Advanced Tools of Data Analytics Training in Bangalore

  • Microsoft Excel – Microsoft Excel is a very popular tool used in data work because it’s simple and powerful. You can use it to clean up data, sort it, and do basic math or checks using formulas. It’s great for quick tasks like making charts or tables to explain your findings. Beginners find Excel a good place to start learning about data.
  • SQL – SQL (Structured Query Language) is a special language used to get and work with data from large databases. It helps you pick out just the parts of the data you need and put them together in useful ways. Big companies use SQL to understand their stored data. Learning SQL is very helpful if you want to work with big sets of data.
  • Python – Python is a simple and flexible computer language used a lot in data jobs. It helps with organizing, calculating, and showing data in easy ways. There are many tools in Python that help you do tasks faster and smarter. Once you learn it, you can also explore things like machine learning later on.
  • Power BI – Power BI is a tool that turns plain data into colorful charts and reports. It lets you look at data from different places and understand it better through visuals. You can build dashboards easily with a simple drag-and-drop feature. Many companies use Power BI to keep track of how their business is doing.
  • Tableau – Tableau is another tool that helps turn data into clear and interesting visuals. It can handle large amounts of data and shows results in easy-to-read charts and maps. You don’t need to know coding to use Tableau, just some practice. It’s great for telling stories with data and helping others understand it quickly.

Top Frameworks Every Data Analytics Should Know

  • Hadoop – Hadoop helps to store and work with huge amounts of data across many computers. It is useful when a company has too much data for a single machine to handle. Hadoop breaks big data into smaller pieces and manages them smartly. This makes it easier and faster to get useful results from large data files.
  • Apache Spark – Spark is used to quickly process big data and get answers faster than many other tools. It works well for tasks like sorting, counting, and finding patterns in data. Many companies use it to save time when working with huge data sets. It also supports both live and saved data for more flexible use.
  • Excel – Excel is one of the most common tools for working with small and medium-sized data. You can organize, calculate, and draw charts easily using it. It’s helpful for beginners who want to start learning about data. Almost every job that uses data includes some work in Excel.
  • Power BI – Power BI is a tool that helps connect different sets of data and show them in simple visuals. You can make dashboards and reports that others can view online. It’s easy to learn and helps people at all levels understand business performance. Many companies use it to track goals, sales, or customer behavior.

Must-Have Skills You’ll Gain in a Data Analytics Course in Bangalore

  • Problem Solving with Data – You’ll develop the ability to solve business or real-world problems by using data. For example, You can use statistics to find the reason for a decline in sales. It will enable you to think clearly and make decisions based on facts rather than assumption. In any industry this makes you an invaluable team player.
  • Data Interpretation and Insight Finding – You will understand how to look at numbers and figures to find useful information. This means noticing patterns, changes or problems in the data. It helps in answering questions like what’s working and what needs improvement. This skill is helpful in any job that involves decision-making.
  • Creating Reports and Dashboards –You will learn how to show your findings using easy-to-read charts, tables or summaries. These tools help others quickly understand the results of your analysis. Good reports help teams take action without needing to study the data themselves. This skill is often used in meetings, presentations or business planning.
  • Communication Skills for Data Sharing – In our Data Analytics Course in Offline, You will learn how to explain your findings in a simple and clear way to people who may not understand data. This includes writing short summaries or speaking about your results. Being able to share insights in an easy way helps your ideas get noticed and used. Good communication turns data into action.
  • Data Cleaning and Preparation – You will learn how to organize messy or incomplete data so it’s easier to work with. This includes removing errors, filling in missing details, and arranging the data properly. Clean data is important because even small mistakes can lead to wrong decisions. This skill is the first and most important step in any data task.

Roles and Responsibilities of Data Analytics Course

  • Data Analyst – A Data Analyst looks at numbers and facts to find useful information. They collect data from different sources, organize it and spot patterns or changes Their job is to help the company understand what is going well and what needs fixing. They often share their findings through charts and reports that others can easily understand.
  • Business Intelligence Analyst – This role focuses on helping companies make smart decisions using data. The analyst gathers information from sales, customer feedback, and operations to find trends. They create dashboards and reports to show how the business is doing. Their main goal is to guide managers and leaders in making better choices.
  • Data Analyst Trainer – The Trainer teaches students or employees how to work with data using tools like Excel, Python, and Power BI. They make hard topics easy to understand and give real-world examples. Trainers also design activities and projects so learners can practice their skills. Their job is to prepare people for careers in data analytics.
  • Marketing Data Analyst – A Marketing Data Analyst studies data from ads, websites, and social media. They identify the campaigns that are effective and those that needto be better. Their role is to assist the marketing team in learning more about consumer behavior and improving the results. They create better marketing plans using this data.
  • Financial Data Analyst – In this role the person works with data related to money, sales and expenses. They help companies understand how money is coming in and whether it’s being spent. Their reports support smart financial decisions and planning. They are playing a key role in budgeting and forecasting future business needs.

The Advantages of Data Analytics for freshers as a Career Option

  • High Demand for Data Skills – Many companies are looking for people who can understand and work with data. Data is used everywhere, from Start ups to large business. As a result, there will be more opportunities for data analytics professionals. Good employment possibilities are accessible to beginners who have the required abilities.
  • No Need for a Technical Background – You don’t need to be an expert in coding or have a tech degree to start learning data analytics. With the right training, anyone can pick up the basics and grow from there. The tools and methods used are easy to learn with practice. This makes it a good choice for students from different study backgrounds.
  • Strong Career Growth – Data analytics is not just a one-time skill it keeps growing with time. As you learn more, you can move into better roles with higher pay. From data analyst to data scientist, there are many paths to explore. It’s a field that offers long-term career growth and stability.
  • Useful Across Industries – Data is used in banking, healthcare, marketing, sports, education, and many more fields. This means you’re not limited to one type of company or job role. You can choose to work in an area that matches your interest. With data skills, you have the freedom to explore different industries.

How Data Analytics Skills Help You Get Remote Jobs

  • Data Work Can Be Done from Anywhere – Most data analytics tasks are computer-based and don’t require you to be in an office. You can clean data, create reports, and analyze trends using just a laptop and internet. This makes it easy for companies to hire remote workers for data roles. If you have the right skills, you can work from home or anywhere in the world.
  • Global Job Opportunities Open Up – With data analytics skills, you’re not limited to jobs in your city or country. Many companies across the globe are looking for skilled analysts who can work remotely. This gives you more chances to find good jobs and better pay. You can apply to roles with startups, tech companies, or international firms.
  • Tools Used in Data Analytics – Popular tools like Excel, Power BI, Python, and SQL can be used online or on your computer. These tools let you share your work easily and work with teams from different locations. You don’t need expensive or heavy software just a strong internet connection. That’s why data analytics is a perfect skill for remote work setups.
  • High Demand in Online Businesses – Many online businesses like e-commerce sites, apps, and digital services need data analysts. They rely on data to understand customer behavior, improve services, and increase sales. These companies often operate online and prefer hiring remote talent. Your data skills can help them grow and help you land remote roles.
  • Freelance and Project Work Options – If you don’t want a full-time remote job, you can still work as a freelancer. Many businesses post short-term data projects on freelance platforms. With strong analytics skills, you can earn money by completing these tasks from home. This gives you freedom, flexibility, and a way to build your career your way.

What to Expect in Your First Data Analytics Job

  • Working with Lots of Data – In your first job, you’ll often deal with large amounts of information from different sources. Your role is to clean, sort, and understand this data so others can use it. At first, it may seem a bit confusing, but you’ll learn with practice. Over time, you’ll become better at spotting useful patterns and trends.
  • Learning New Tools on the Job – Even if you’ve learned tools like Excel, SQL, or Power BI during training, you might use new ones at work. Companies often have their own tools or systems for data. You’ll get time to learn these tools, and your team will help you. Being open to learning is more important than knowing everything right away.
  • Supporting Business Decisions – In your first role, your job is to help the company understand what the data is saying. You might be asked to find out what’s working well or what needs improvement. Your analysis helps managers make smart choices based on facts, not guesses. Even simple reports from you can guide big decisions in the company.
  • Teamwork and Communication – Data analysts often work with other teams like marketing, sales, or finance. You’ll need to explain your findings in a simple way that anyone can understand. You’ll also join meetings and share ideas with your team. Good communication helps others see the value in your work.

Top Companies Hiring Data Analytics Professionals

  • Google –Google uses data to improve search results, ads, and user experience across its products. They hire data analysts to study user behavior, measure performance, and support business decisions. Working at Google gives freshers a chance to work on global-level projects. It’s one of the best places to grow in a data-driven role.
  • Amazon – Amazon depends on data to manage its huge online store, track customer habits, and manage deliveries. They need data professionals to help with pricing, sales trends, and product suggestions. Amazon offers many opportunities for freshers in data roles. It’s a fast-paced environment with room to learn and grow.
  • TCS – TCS (Tata Consultancy Services) works with many clients across the world and uses data to improve business services. They hire data analysts to support banking, healthcare, retail, and more industries. TCS is known for training freshers and helping them build strong careers. It’s a great starting point for those new to data analytics.
  • Accenture – Accenture helps businesses to use data to become more efficient and solve problems. They hire data experts to study trends, create reports and support digital transformation. Freshers get to work with global clients and gain real experience quickly. The company also offers regular training to help you grow in your role.
  • Infosys – Infosys uses data to support software development, client insights and business strategies. They hiring freshers in data analytics to assist with data handling, reporting and project planning. The company offers a strong learning environment and career development. It is a reliable place to start your journey in data analytics.
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Upcoming Batchs For Classroom and Online

Weekdays
07-July-2025
08:00 AM & 10:00 AM
Weekdays
09-July-2025
08:00 AM & 10:00 AM
Weekends
12-July-2025
(10:00 AM - 01:30 PM)
Weekends
13-July-2025
(09:00 AM - 02:00 PM)
Can't find a batch you were looking for?
INR ₹14000
INR ₹18000

OFF Expires in

Who Should Take a Data Analytics Course

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Working Professionals

Diploma Holders

Professionals from Other Fields

Salary Hike

Graduates with Less Than 60%

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Job Roles For Data Analytics Training in Bangalore

Data Analyst

Business Analyst

Data Scientist

Data Engineer

BI Analyst

Marketing Analyst

Financial Analyst

Operations Analyst

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Tools Covered For Data Analytics Training

Apache Spark Power BI Tableau Data Studio Excel SQL R Programming Python

What’s included ?

Convenient learning format

📊 Aptitude and Technical Skills Training

  • Learn basic maths and logical thinking to solve problems easily.
  • Understand simple coding and technical concepts step by step.
  • Get ready for exams and interviews with regular practice.
Dedicated career services

🛠️ Hands-On Projects

  • Work on real-time projects to apply what you learn.
  • Build mini apps and tools to boost your skills.
  • Gain practical experience just like in real jobs.
Learn from the best

🧠 AI Powered Self Interview Practice Portal

  • Practice interview questions with instant AI feedback.
  • Improve your answers by speaking and reviewing them.
  • Build confidence with real-time mock interview sessions.
Learn from the best

🎯 Interview Preparation For Freshers

  • Practice company-based interview questions.
  • Take online assessment tests to crack interviews
  • Prepare effectively with real-world questions.
Learn from the best

🧪 LMS Online Learning Platform

  • Watch top trainer's videos and documents.
  • Learn anytime with videos and documents.
  • Quickly find topics with organized learning materials.

Data Analytics Course Syllabus in Bangalore

  • 🏫 Classroom Training
  • 💻 Online Training
  • 🚫 No Pre Request (Any Vertical)
  • 🏭 Industrial Expert

Students enrolling in the Data Analytics Training in Bangalore have the option to focus on a specific area that matches their interests and career plans. This flexible learning path helps them gain deep skills in areas like data visualization, business analytics, or data processing, while still covering all the essential topics in the Data Analytics course.

  • Data Analytics with Python – Focuses on Python programming with libraries like Pandas, NumPy, and Matplotlib for effective data manipulation and visualization.
  • Data Analytics with R – Covers R programming for statistical computing, data analysis, and graphical representation in various domains.
  • Business Analytics Track – Emphasizes tools like Excel, Power BI, and SQL to extract business insights and support decision-making.
  • Machine Learning Track – Includes hands-on training in algorithms, data modeling, and predictive analytics using Python and scikit-learn.
Fundamentals of Data Analytics
Excel for Data Analysis
SQL for Data Querying
Python for Data Analytics
Data Visualization Tools
Basics of Machine Learning
Statistics for Data Analytics

These form the foundation of understanding data and analytics:

  • Types of Data – Structured, semi-structured, and unstructured data.
  • Analytics Types – Descriptive, diagnostic, predictive, prescriptive.
  • Data Lifecycle – Collection, cleaning, analysis, visualization, interpretation.
  • Roles in Analytics – Data analyst, business analyst, data scientist.

These are used for basic data manipulation and visualization:

  • Formulas & Functions – SUM, IF, VLOOKUP, INDEX and MATCH.
  • Data Cleaning Tools – Remove duplicates, text-to-columns, data validation.
  • Pivot Tables – Summarize and explore large datasets
  • Charts – Column, bar, line, pie, combo charts for visualization

These are used to interact with relational databases:

  • SELECT Queries – Retrieve specific data from tables.
  • JOINs – Combine data from multiple tables (INNER, LEFT, RIGHT)
  • GROUP BY & Aggregations – SUM, AVG, COUNT for grouped data
  • Subqueries & Aliasing – Use queries within queries and rename columns.

These libraries are used for programming and data operations:

  • NumPy – Numerical computations and array handling
  • Pandas – Dataframes for reading, transforming, and analyzing data
  • Matplotlib– Basic charting and visualizations
  • Seaborn– Statistical data visualizations with styling options

These are used to create dashboards and interactive reports:

  • Power BI – Microsoft’s business intelligence tool.
  • Tableau – Visual analytics platform for building dashboards.
  • Filters & Slicers – Interactive controls for data exploration.
  • Calculated Fields – Custom formulas within visuals

These are used to apply predictive analytics and modeling:

  • Scikit-learn – Python library for machine learning.
  • Supervised Learning – Regression, classification (e.g., linear regression, decision trees).
  • Unsupervised Learning – Clustering techniques like K-Means.
  • Model Evaluation – Accuracy, confusion matrix, cross-validation.

These concepts help understand patterns and support decision-making:

  • Descriptive Statistics – Mean, median, mode, range, standard deviation
  • Probability – Basic probability, distributions, conditional probability.
  • Inferential Statistics – Hypothesis testing, confidence intervals, t-tests.
  • Environment Configuration – Set up ports, secrets and variables in hosting platforms.
  • Correlation and Regression – Relationships and prediction between variables.

🎁 Free Addon Programs

Aptitude, Spoken English

🎯 Our Placement Activities

Daily Task, Soft Skills, Projects, Group Discussions, Resume Prepration, Mock Interview

Get Real-Time Experience in Data Analytics Projects

Placement Support Overview

Today's Top Job Openings for Data Analytics Professionals

Associate - Data Analyst

Company Code: LCT137

Bengaluru

₹7LPA - ₹10LPA a year

Any Degree

Exp 0-2 yrs

  • We are looking for passionate and detail-oriented fresher to join our Customer Success team as an Associate Data Analyst. You will support data-driven strategies for top retail clients by analyzing customer behavior and campaign performance. This role involves working with SQL, Python, and visualization..
  • Easy Apply

    Financial Data Analyst

    Company Code: MIS664

    Bengaluru

    ₹20,000 - ₹30,000 a month

    Any Degree

    Exp 0-5 yr

  • Now hiring for a detail oriented professional with a strong background in accounts and financial analysis to support credit and data review processes. The role involves preparing financial inputs, analyzing statements, updating reports and assisting in portfolio monitoring.
  • Easy Apply

    Junior Analyst

    Company Code: INP230

    Bengaluru

    ₹2LPA - ₹3LPA a year

    Any Degree

    Exp 0-1 yr

  • Exciting opportunity for a Junior Analyst to join our data team and support the development of interactive dashboards using Power BI. You’ll work on transforming raw data into meaningful insights, ensuring data accuracy, and optimizing report performance.
  • Easy Apply

    Market Research Data Analyst

    Company Code: RRH675

    Bengaluru

    ₹5LPA - ₹8LPA a year

    Any Degree

    Exp 0-2 yrs

  • Seeking candidates for a detail-oriented Data Analyst with strong communication and analytical skills. You will manage multiple tasks, explain findings clearly to research teams, and support them in creating client-friendly reports. Your insights will help turn complex data into simple and useful information.
  • Easy Apply

    Data Analyst

    Company Code: ASD287

    Bengaluru

    ₹6LPA - ₹ 7LPA a year

    Any Degree

    Exp 0-2 yrs

  • Open positions available for a skilled Data Analyst to manage and analyze large datasets, ensure data accuracy and support business decisions with meaningful insights. This role involves maintaining data system, developing reports, dashboards and improving data quality. Candidates should have experience with IBM SPSS and data integration tools.
  • Easy Apply

    Business Intelligence Analyst

    Company Code: EXX765

    Bengaluru, Karnataka

    ₹7LPA - ₹10LPA a year

    Any degree

    Exp 0-1 yrs

  • We're recruiting for a Data Analytics professional who can collaborate with Product Management to plan and prioritize key deliverables. The ideal candidate should be skilled at turning business needs into data-driven solutions and have hands-on experience with Agile tools and methods.
  • Easy Apply

    Data Engineer

    Company Code: VKT713

    Bengaluru

    ₹5LPA - ₹20LPA a year

    Any Degree

    Exp 0-2 yrs

  • Join our team as a skilled Data Engineer to designing and maintain the robust data pipelines and infrastructure. The role involves working with both batch and real time data from various sources.
  • Easy Apply

    Business Analyst Junior

    Company Code: APZ812

    Bengaluru

    ₹25,000 - ₹50,000 a month

    Any Degree

    Exp 0-1 yrs

  • Now accepting applications for a Junior Business Analyst to join our team and support business improvement initiatives. You will evaluate processes, gather requirements, and help develop effective solutions.
  • Easy Apply

    Highlights for Data Analytics Internship

    Real-Time Projects

    • 1. Gain hands-on experience by working on live industry-based applications.
    • 2. Understand real-world problem-solving through Data Analytics scenarios.
    Book Session

    Skill Development Workshops

    • 1. Participate in focused sessions on trending technologies and tools.
    • 2. Learn directly from industry experts through guided practical exercises.
    Book Session

    Employee Welfare

    • 1. Enjoy benefits like health coverage, flexible hours, and wellness programs.
    • 2. Companies prioritize mental well-being and work-life balance for all employees.
    Book Session

    Mentorship & Peer Learning

    • 1. Learn under experienced mentors who guide your technical and career growth.
    • 2. Collaborate with peers to enhance learning through code reviews and group projects.
    Book Session

    Soft Skills & Career Readiness

    • 1. Improve communication, teamwork, and time management skills.
    • 2. Prepare for interviews and workplace dynamics with mock sessions and guidance.
    Book Session

    Certification

    • 1. Earn recognized credentials to validate your Data Analytics skills.
    • 2. Boost your resume with course or project completion certificates from reputed platforms.
    Book Session

    Sample Resume for Data Analytics (Fresher)

    • 1. Simple and Neat Resume Format

      – Use a clean layout with clear sections like summary, skills, education, and projects.

    • 2. List of Technologies You Know

      – Mention skills like Excel, SQL, Python, Power BI, Tableau, Data Visualization, and Data Cleaning tools.

    • 3. Real-Time Projects and Achievements

      – Add 1–2 real-time projects with a short description and the tools used.

    Top Data Analytics Interview Questions and Answers (2025 Guide)

    Ans:

    In order to extract insights from data, data analysis is a systematic process that includes handling data through operations like intake, cleaning, transformation, and assessment. To begin, information is gathered from various sources. Because the data is raw, it must be cleaned and processed in order to fill in missing values and remove any entities that are no longer relevant.

    Ans:

    The process of thoroughly examining every entity found in data is known as data profiling. Providing extremely accurate information based on data and its characteristics, including datatype, frequency of occurrence, and more, is the aim here.

    Ans:

    Data validation is the process that involves the determining the accuracy of data and the quality of source as well. There are many processes in data validation but The two most important are data screening and data verification.

    • Data screening: Making use of variety of models to ensure that data is accurate and no redundancies are present.
    • Data verification: If there is redundancy, it is evaluated based on the multiple steps and then a call is taken to ensure presence of the data item.

    Ans:

    • Data analysis is the process of cleaning, organizing, and utilizing data to generate meaningful insights. Data mining is a technique for discovering hidden patterns in data.
    • Data analysis yields results that are far more understandable to a wide range of audiences than data mining.

    Ans:

    • Google Search Operators
    • RapidMiner
    • Tableau
    • KNIME
    • OpenRefine

    Ans:

    An outlier is the value in a dataset that is considered to be away from mean of the characteristic feature of a dataset. There are two types of the outliers: univariate and multivariate.

    Ans:

    • A well-designed model should be predictably accurate. This relates to the ability to predict future insights when they are required.
    • A rounded model easily adapts to any changes made to the data or pipeline if necessary.
    • The model should have ability to cope in case there is immediate requirement to large-scale data.
    • The model’s operation should be simple and easily understood by clients in order to help them achieve the desired results.

    Ans:

    Data is a constantly evolving entity. A company’s growth may result in unforeseen opportunities that necessitate updating the data. Additionally, evaluating the model to determine its standing can assist analysts in determining whether or not a model needs to be retrained.

    Ans:

    Data Cleaning, also known as Data Wrangling, is a structured method of locating and safely removing erroneous content in data to ensure that data is of the highest quality.

    Ans:

    One of the most important aspects of Excel is pivot tables. They enable easy viewing and summarization of a large dataset by the user. The majority of actions with pivot tables involve drag-and-drop functionality, which facilitates rapid report creation.

    Company-Specific Interview Questions from Top MNCs

    1. What is data science and how is it different from data analytics?

    Ans:

    The process of collecting, analyzing, and interpreting large data sets using various instruments and methods is known as data science. Data analytics focuses more on analyzing existing data to find trends and solve problems. Data science is broader and includes data analytics, machine learning, and predictive modeling.

    2. What does a data scientist do in a company?

    Ans:

    A data scientist helps companies make smart decisions by analyzing data, creating models, and finding useful patterns. They help solve business problems using data.

    3. What’s the difference between structured and unstructured data?

    Ans:

    Structured data is the organized in rows and columns such as Excel sheets or databases. Unstructured data doesn’t follow a clear format like emails, images, or videos.

    4. What are a data science projects key steps?

    Ans:

    • Understanding the problem
    • Collecting data
    • Cleaning data
    • Analyzing it
    • Building models
    • Interpreting the results

    5. How do you deal with missing values in data?

    Ans:

    You can remove rows with missing values, fill them with the average or most common value, or predict them using other data.

    6. What differentiates supervised learning and unsupervised learning?

    Ans:

    Supervised learning uses the labeled data to train the model (we know the correct answers). Unsupervised learning finds patterns in data without labels.

    7. What is cross-validation?

    Ans:

    It’s a method to test if your model works well on different data by splitting data into parts and testing the model on each part.

    8. What is a confusion matrix?

    Ans:

    It’s a table that shows how well a classification model performed. It includes true positives, true negatives, false positives, and false negatives.

    9. How do you choose which features are important?

    Ans:

    You can use techniques like correlation, importance scores from models, or removing features one by one to see their impact.

    10. How does KNN (k-nearest neighbors) work?

    Ans:

    KNN finds the closest data points to a new point and predicts its value based on them. It’s like asking nearby neighbors for advice.

    11. How do decision trees work?

    Ans:

    A decision tree splits data into branches based on questions. Each step leads to a decision until the final result is reached.

    12. What is SVM and where is it used?

    Ans:

    Support Vector Machine (SVM) is a model that finds the best line or boundary to separate different classes in data. It’s used in image recognition, spam detection, etc.

    13. How does Naive Bayes work?

    Ans:

    Naive Bayes predicts outcomes based on past data using simple probabilities. It assumes all features are independent.

    14. What is k-means clustering used for?

    Ans:

    K-means groups similar data points together into clusters. It’s useful for customer segmentation, grouping similar users, etc.

    15. Describe the neural network.

    Ans:

    A neural network is the model that inspired by the human brain. It takes inputs, processes them through layers, and gives an output. It is used in image and voice recognition.

    16. Describe the neural network.

    Ans:

    These are techniques that combine many models to improve accuracy. Examples are Random Forest and Gradient Boosting.

    17. How do you manage outliers in data?

    Ans:

    You can remove, transform, or adjust outliers. Sometimes, they are important and need to be studied carefully.

    18. What are some ways to scale features?

    Ans:

    Scaling means bringing all values to the same range using methods like normalization or standardization.

    19. What is one-hot encoding?

    Ans:

    It converts categories into numbers using 0s and 1s so that machine learning models can understand them.

    20. Why is feature selection important?

    Ans:

    It helps remove unnecessary data, reduces time and improves the model performance by focusing only on the important parts.

    1. What does a data analyst do?

    Ans:

    A data analyst looks at data to find patterns and insights. They are helps companies to make smart decisions based on facts and numbers.

    2. How do you make sure your data is accurate and trustworthy?

    Ans:

    I will check for errors, remove duplicates, fix missing values, and use validation rules to keep the data clean and reliable.

    3. What is data cleaning and why do we need it?

    Ans:

    Data cleaning means fixing incorrect, missing, or messy data. It's needed because clean data gives correct results and better decisions.

    4. What tools do you use for working with data?

    Ans:

    • Excel for simple tasks
    • SQL for databases
    • Power BI or Tableau for visuals
    • Python for deeper analysis

    5. What’s the difference between a primary key and a foreign key in SQL?

    Ans:

    A primary key are uniquely identifies each row in a table. A foreign key connects one table to another using the primary key from that table.

    6. How do you deal with missing or incomplete data?

    Ans:

    I either remove it, fill it with average or common values, or use other logic to estimate what the missing data should be.

    7. Can you explain data normalization simply?

    Ans:

    Normalization is a way to organize data in a database so there’s no duplicate info, and everything is stored efficiently.

    8. What is a pivot table in Excel and how do you use it?

    Ans:

    A pivot table helps summarize large data sets. I use it to quickly count, sum, or compare values from the data.

    9. What’s the difference between correlation and causation?

    Ans:

    Correlation means two things happen together. Causation means one thing causes the other to happen.

    10. Why is data visualization important?

    Ans:

    It makes data easier to understand using charts and graphs. People can quickly see trends, patterns, and insights.

    11. How would you explain a data project to someone who isn’t technical?

    Ans:

    I use simple words, charts, and real-life examples so they can easily understand the main points and results.

    12. What is regression analysis and what types are there?

    Ans:

    Regression analysis predicts values using other data. Common types are linear (straight-line prediction) and logistic (used for yes/no outcomes).

    13. What is ETL and why is it used?

    Ans:

    ETL means Extract, Transform, Load. It’s the process of taking data from one place, cleaning it, and putting it into another system.

    14. How do you make sure your data results are correct?

    Ans:

    I check data sources, clean data carefully, test my steps, and confirm results with different methods.

    15. What is A/B testing and how is it used?

    Ans:

    A/B testing compares two options (like two website designs) to see which one performs better with users.

    1. Which tools do data analysts often use?

    Ans:

    Data analysts commonly use Excel, SQL, Python, R, Power BI, and Tableau to analyze, clean, and visualize data.

    2. What do you do when some data is missing in a dataset?

    Ans:

    I either remove the missing values, fill them in with an average or guessed value, or use tools to predict the missing data based on other information.

    3. What is the difference between a database and a data warehouse?

    Ans:

    A database stores current data for daily use. A data warehouse are stores large amounts of historical data for reporting and analysis.

    4. Why is cleaning data important in analysis?

    Ans:

    Clean data gives accurate results. If the data is messy or wrong, the analysis will also be wrong.

    5. What is data normalization and why do we need it?

    Ans:

    Normalization means organizing data properly in a database to avoid repeating information and make data easier to manage.

    6. How do you make a pivot table in Excel?

    Ans:

    You select your data, go to the “Insert” tab, and click on “Pivot Table.” Then you drag fields to rows, columns, and values to see summaries.

    7. What is a SQL join, and what types are there?

    Ans:

    A join in SQL connects data from two or more tables.

    • INNER JOIN
    • LEFT JOIN
    • RIGHT JOIN
    • FULL JOIN

    8. What is data visualization and why is it useful?

    Ans:

    It means showing data using charts and graphs. It helps people understand patterns and trends easily.

    9. How do you check if your data is correct (data validation)?

    Ans:

    I set rules or limits to make sure the data is accurat for example, checking that age is a number or dates are not in the future.

    10. What does data modeling mean?

    Ans:

    It’s the process of designing how data will be stored, connected, and organized in a system, like making a blueprint for the data.

    11. How do you handle a project with a lot of messy or unstructured data?

    Ans:

    I start by understanding the data, organizing it, converting it into a structured format and then analyzing it step by step.

    12. What is ETL in data processing?

    Ans:

    ETL stands for Extract, Transform, Load. It means taking data from different sources, cleaning it and storing it in a database or warehouse.

    13. How would you explain data mining to someone without technical knowledge?

    Ans:

    Data mining is like digging through large amounts of data to find useful information, patterns, or trends that help make decisions.

    14. What are some basic statistics used in data analysis?

    Ans:

    • Mean (average)
    • median (middle value)
    • mode (most frequent)
    • standard deviation (spread of data)
    • correlation (relationship between values)

    15. How do you know if your analysis is good or not?

    Ans:

    I check the data quality, test the results, review the accuracy, and ask others to review it. I also make sure it solves the business question clearly.

    1. Which tools do you use for analyzing data?

    Ans:

    I commonly use Excel, SQL, Power BI, Tableau, and Python for different types of data tasks like cleaning, analyzing, and visualizing.

    2. What do you do when some data is missing in your dataset?

    Ans:

    I either remove rows with missing data, fill them using the average or most common value, or use logical methods to guess what fits best.

    3. What is regression analysis?

    Ans:

    It’s a method to understand how one thing affects another like how sales depend on advertising. It helps in making predictions.

    4. What are the types of regression techniques?

    Ans:

    Common ones are linear regression (straight-line relationship), logistic regression (for yes/no outcomes), and polynomial regression (curved relationships).

    5. How do you make sure your data is accurate?

    Ans:

    I clean the data, check for errors, remove duplicates, and use tools to validate and cross-check the information.

    6. What is data normalization and why is it used?

    Ans:

    Normalization organizes data properly in a database to reduce repetition and ensure it’s easy to use and manage.

    7. What is a pivot table in simple words?

    Ans:

    It’s a tool in Excel that helps quickly summarize and analyze large data, like total sales by region.

    8. What does p-value mean in statistics?

    Ans:

    A p-value helps tell if your result is meaningful or just happened by chance. A small p-value usually means your findings are significant.

    9. Tell me about a tough data project you worked on.

    Ans:

    I once worked on a project with messy and huge data. I had to clean, organize, and find patterns. It took time, but the insights helped improve business performance.

    10. What is SQL and why is it useful?

    Ans:

    SQL is a language used to storing and processing the databases. It helps find, add or change data easily and is important for working with structured data.

    11. What is a data warehouse?

    Ans:

    A data warehouse is a big storage system that holds large amounts of historical data, which companies use for analysis and reports.

    12. What ethical rules should a data analyst follow?

    Ans:

    Be honest with data, keep private info safe, and avoid changing results to mislead others.

    13. How do you stay updated with trends in data analytics?

    Ans:

    I follow blogs, take online courses, join webinars, and read about the latest tools and techniques in data.

    14. How do you handle outliers in your data?

    Ans:

    I first check if they are real or errors. If they are mistakes, I fix or remove them. If they’re valid, I analyze their impact on the results.

    1. Who is a data analyst and what do they do?

    Ans:

    A data analyst looks at data to find useful information that helps businesses to make the smart decisions.

    2. How is data analysis different from data science?

    Ans:

    Data analysis finds trends and answers in existing data. Data science includes that, plus building models to predict future trends.

    3. What tools do you use to analyze data?

    Ans:

    I use tools like Excel, SQL, Power BI, Python, R, and Tableau depending on the task.

    4. What is SQL and how is it used?

    Ans:

    SQL is a language to work with databases. It helps you get, update, and manage data.

    5. What is primary key in a database?

    Ans:

    A primary key acts as a unique ID for every table row. It enables the organization and connectivity of data.

    6. What are some common data formats?

    Ans:

    Some common ones are CSV, Excel, JSON, and XML — used to store and share data.

    7. How do you check if data is good or bad?

    Ans:

    I look for missing values, duplicates, wrong formats, and check if it makes sense overall.

    8. What’s the role of a data analyst in a team?

    Ans:

    A data analyst works with team members to understand goals, analyze data, and give insights to guide decisions.

    9. Why is it important to use data to make decisions?

    Ans:

    Data-based decisions are based on facts, not guesses, and lead to better results.

    10. How do you make sure your data is correct and reliable?

    Ans:

    I clean the data, remove errors, use checks, and verify results before using it.

    11. What is data normalization?

    Ans:

    It means organizing data in a database to avoid repetition and improve storage efficiency.

    12. What is the difference between data wrangling and data cleaning?

    Ans:

    Data cleaning fixes errors. Data wrangling includes cleaning and also reshaping or changing data to make it ready for analysis.

    13. What do you do if there is duplicate data?

    Ans:

    I identify and remove duplicates to make sure each entry is unique.

    14. Which tools help in preparing data?

    Ans:

    • Excel
    • Python (like Pandas)
    • Power Query
    • SQL to clean

    15. What are outliers, and how do you deal with them?

    Ans:

    Outliers are values very different from others. I study them to see if they are mistakes or meaningful, then decide to remove or adjust them.

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    The details mentioned here are for supportive purposes only. There are no tie-ups or links with the corresponding PGs.

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    Top Data Analytics Job Opportunities for Freshers

    • 1. Junior Data Analyst Jobs at Startups and IT Companies
    • 2. Campus Placements and IT Service Jobs
    • 3. Internship-to-Job Programs
    • 4. Apply Through Job Portals
    • 5. Skills That Help You Get Hired

    Getting Started With Data Analytics Course in Bangalore

    Easy Coding
    8 Lakhs+ CTC
    No Work Pressure
    WFH Jobs (Remote)

    Why Data Analytics is the Ultimate Career Choice

    High Demand

    Companies prefer multi-skilled professionals who can handle entire project cycles.

    Global Opportunities

    Open doors to remote and international job markets.

    High Salary

    Enjoy competitive salaries and rapid career advancement.

    Flexible Career Path

    Explore roles such as developer, architect, freelancer, or entrepreneur.

    Future-Proof Career

    Stay relevant with skills that are consistently in demand in the evolving tech landscape.

    Versatility Across Industries

    Work in various domains like e-commerce, healthcare, finance, and more.

    Career Support

    Placement Assistance

    Exclusive access to ACTE Job portal

    Mock Interview Preparation

    1 on 1 Career Mentoring Sessions

    Career Oriented Sessions

    Resume & LinkedIn Profile Building

    Get Advanced Data Analytics Certification

    You'll receive a certificate proving your industry readiness.Just complete your projects and pass the pre-placement assessment.This certification validates your skills and prepares you for real-world roles.

    • Google Data Analytics Certification
    • Microsoft Power BI Certification
    • IBM Data Analyst Certification
    • SAS Analytics Certification
    • Tableau Specialist Certification
    • AWS Data Analytics Certification

    Getting a data analytics certification can greatly enhance your job prospects. It shows your dedication to learning confirms your technical expertise, and helps your resume stand out. Employers see certification as evidence that you're prepared for the role though hands-on experience strong problem solving capacities and effective communication skills are also essential for securing a position.

    Usually 3 to 6 months, depending on your schedule. Some fast-track programs take less time, while part-time learning may take longer.

    • Builds your reputation and helps others recognize your skills in the industry
    • It shows that you know how to use essential tools and methods effectively
    • It opens up more job chances and may help you earn a higher salary
    • Boosts your confidence to solve real-world challenges using data
    • It gives you a clear learning path with practical, hands-on experience
    • Work often with real-life data examples to build confidence.
    • Become proficient in using Excel, SQL, Python, and Tableau for data analysis.
    • Try practicing exams and reviewing past questions to prepare for the test.
    • Learn the ideas deeply rather than just trying to remember facts.

    Complete Your Course

    A Downloadable Certificate in PDF Format, Immediately Available to You When You Complete Your Course

    Get Certified

    A Physical Version of Your Officially Branded and Security-Marked Certificate.

    Get Certified

    Lowest Data Analytics Fees in Bangalore

    Affordable, Quality Training for Freshers to Launch IT Careers & Land Top Placements.

    Call Course Advisor

    How is ACTE's Data Analytics Course in Bangalore Different?

    Feature

    ACTE Technologies

    Other Institutes

    Affordable Fees

    Competitive Pricing With Flexible Payment Options.

    Higher Data Analytics Fees With Limited Payment Options.

    Industry Experts

    Well Experienced Trainer From a Relevant Field With Practical Data Analytics Training

    Theoretical Class With Limited Practical

    Updated Syllabus

    Updated and Industry-relevant Data Analytics Course Curriculum With Hands-on Learning.

    Outdated Curriculum With Limited Practical Training.

    Hands-on projects

    Real-world Data Analytics Projects With Live Case Studies and Collaboration With Companies.

    Basic Projects With Limited Real-world Application.

    Certification

    Industry-recognized Data Analytics Certifications With Global Validity.

    Basic Data Analytics Certifications With Limited Recognition.

    Placement Support

    Strong Placement Support With Tie-ups With Top Companies and Mock Interviews.

    Basic Placement Support

    Industry Partnerships

    Strong Ties With Top Tech Companies for Internships and Placements

    No Partnerships, Limited Opportunities

    Batch Size

    Small Batch Sizes for Personalized Attention.

    Large Batch Sizes With Limited Individual Focus.

    LMS Features

    Lifetime Access Course video Materials in LMS, Online Interview Practice, upload resumes in Placement Portal.

    No LMS Features or Perks.

    Training Support

    Dedicated Mentors, 24/7 Doubt Resolution, and Personalized Guidance.

    Limited Mentor Support and No After-hours Assistance.

    Data Analytics Course FAQs

    1. What do I need to become a Data Analyst?

    You should have basic math and logical thinking skills. Knowing Excel and a little bit of coding (like Python or R) can help. A college degree is nice but not always needed. Most important is your interest in data and willingness to learn
    Yes, most training programs give you a certificate when you finish. You can use this certificate on your resume or LinkedIn to show your new skills.
    The training covers backend and frontend technologies, such as:
    • HTML, CSS, JavaScript
    • React or Angular (for frontend)
    • Node.js, Express.js (for backend)
    • MongoDB, MySQL (for databases)
    • Git, REST APIs, and tools for launching websites
    Yes, you’ll work on real-world projects like sales analysis, dashboards, or customer reports. These help you practice your skills and build a good portfolio.
    Yes, ACTE gives one-on-one help to make your resume better and show your Data Analytics skills clearly to employers.
    Anyone who likes working with data and solving problems can join. Freshers, working professionals, and even business managers are welcome. No coding experience is needed.
    Not necessarily. A degree can help but many people succeed without one. Skills and practical knowledge are more important.
    Basic computer use, comfort with numbers, and interest in data. Knowing Excel is helpful. Coding isn’t needed at the start.
    No. Data analytics is different from web development. You don’t need to know frontend or backend coding.

    1. What kind of Data Analytics Placement support will I get?

    Training centers help with resume writing, interviews, and sometimes connect you to job openings. Some have job portals or direct links with companies.

    2. Will I get projects for my resume?

    Yes. You’ll complete real projects that you can show on your resume. These help prove your skills.

    3. Can I apply to top IT companies after the Data Analytics Training?

    Yes. With the right skills and a strong portfolio, you can apply to well-known companies. Certification and hands-on experience help you stand out.

    4. Is support available for freshers?

    Yes. Many institutes focus on freshers and offer special help for those starting out with no work experience.
    • Google Data Analytics Certification
    • Microsoft Power BI Certification
    • IBM Data Analyst Certification
    • SAS Analytics Certification
    • Tableau Specialist Certification
    • AWS Data Analytics Certification
    Getting a data analytics certification can greatly enhance your job prospects. It shows your dedication to learning confirms your technical expertise, and helps your resume stand out. Employers see certification as evidence that you're prepared for the role though hands-on experience strong problem solving capacities and effective communication skills are also essential for securing a position.
    Usually 3 to 6 months, depending on your schedule. Some fast-track programs take less time, while part-time learning may take longer.
    • Builds your reputation and helps others recognize your skills in the industry
    • It shows that you know how to use essential tools and methods effectively
    • It opens up more job chances and may help you earn a higher salary
    • Boosts your confidence to solve real-world challenges using data
    • It gives you a clear learning path with practical, hands-on experience
    • Work often with real-life data examples to build confidence.
    • Become proficient in using Excel, SQL, Python, and Tableau for data analysis.
    • Try practicing exams and reviewing past questions to prepare for the test.
    • Learn the ideas deeply rather than just trying to remember facts.

    1. Will I get job support after the Data Analytics course?

    Yes, you will get a placement support from ACTE and it offers job help, including resume sessions, interview practice, and job leads. Some also work with recruiters directly.
    Prices vary based on trainer experience, course content, tools taught, and extra services like projects or job support.
    Yes, Most are affordable and offer flexible payments or EMI options. Compare course value, not just price.
    Yes, we charge the same fee in every location. Whether you’re in a big city or a small town, the price and quality of our training remain consistent.

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