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Data Analytics Course in Anna nagar

Enroll in the Top Data Analytics Training Institute in Anna Nagarto Master in Data Handling.
Complete Data Analytics Training in Anna Nagar Covers Excel, SQL, Python, Power BI.
Work on Real-time Industry Projects to Gain Practical, Hands-on Experience.
Flexible Learning Options Choose From Weekday, Weekend, or Fast-track Batches.
Career-focused Data Analytics Certification Course in Anna Nagar With Placement Support.
Get Expert Guidance on Resume Building, Interview Preparation and Career Planning.

WANT IT JOB

Become a Data Analyst in 3 Months

Freshers Salary

3 LPA

8 LPA

Quality Training With Affordable Fees in Anna Nagar!

INR ~~₹32000~~

INR ₹18500

11280+

(Placed)

Freshers To IT

5845+

(Placed)

NON-IT to IT

8955+

(Placed)

Career Gap

4876+

(Placed)

Less Then 60%

550+ Students Placed Every Month Be The Next!

Our Hiring Partners

Overview of Data Analytics Course

The Data Analytics Course in Anna Nagar is designed for freshers who want to build a strong foundation in data skills. This beginner-friendly Data Analytics Training in Anna Nagar covers essential tools like Excel, SQL, Python, Power BI and basic Machine Learning. You’ll learn how to collect, clean, analyze and visualize data to make informed decisions. The course includes real-time Data Analytics Projects that give you practical experience and confidence. A recognized Data Analytics Certification will be awarded upon course completion. We also provide full Data Analytics Placement support including resume building and interview preparation.

What You'll Learn From Data Analytics Training

Build strong data analytics skills through our comprehensive Data Analytics Course in Anna Nagar, covering tools like Excel, SQL, Python and Power BI.

Understand core concepts such as data cleaning, visualization techniques and statistical methods to solve real-world problems.

Work on hands-on Data Analytics Projects that reflect real industry challenges and enhance your practical knowledge.

Learn advanced topics like data modeling, automation and dashboard creation for data-driven business decisions.

Grow from beginner to advanced levels, gaining confidence to interpret and present insights with clarity and impact.

Join our expert-led Data Analytics Training in Anna Nagar and earn a recognized certification to accelerate your career with placement assistance.

Additional Info

Course Highlights

Select from focused learning paths like Excel for analytics, SQL essentials, Python programming and Power BI dashboards.
Get full Data Analytics Placement guidance, resume building and mock interviews to connect you with top employers and hiring startups.
Be a part of a growing ecosystem with thousands of trained professionals and partnerships with 300+ hiring companies.
Learn how to work with data by doing step-by-step activities that help you understand and solve real problems easily.
Learn through a practical, job-oriented syllabus built by industry experts, including certification and career support.
Get practical experience by working on industry scenarios, real-world projects and portfolio-building activities.
Train under certified instructors with over a decade of hands-on experience in delivering expert Data Analytics Training in Anna Nagar.

Essential Benefits of Data Analytics Course

Smarter Planning – Data Analytics helps people and companies plan better by using real information instead of guesses. When you understand patterns in past data it becomes easier to know what might happen next This helps in setting goals and making safer decisions. For example, a store can use data to plan how much stock to buy each season.
Clearer Insights – Analytics makes complex information easier to understand by breaking it down into useful parts. Charts, graphs and simple numbers can show what’s really happening. This helps teams focus on the right tasks and avoid confusion. For example, a school can use analytics to see which subjects students struggle with.
Personalized Experience – With data, businesses can offer services that match each customer’s needs. Analytics helps track what people like, buy or search for. This makes it easier to suggest things they will enjoy. For instance, apps can show users songs, videos or products based on their past choices.
Saves Time and Money – Data Analytics can show which parts of a process take too long or cost too much. This helps teams make changes that speed things up and save resources. For example, a delivery company can use data to find the fastest routes. Or a business can stop spending money on things that don’t work

Master Powerful Tools in Data Analytics Training in Anna Nagar

Excel – Excel is a user-friendly tool that helps you work with numbers, text and tables. You can easily sort, filter and organize data to find useful information. It's great for making charts and quick summaries without needing any programming. Excel is often the first step in learning how to manage and understand data. Its simple yet powerful for everyday analytics tasks.
SQL – SQL (Structured Query Language) helps you find and manage information stored in large databases. With just few commands, you can pull out the exact data you need from thousands of records. Its useful for checking sales reports, customer details, or inventory data. Learning SQL helps you talk directly to the database in a simple way. It’s a must-have skill for many data jobs.
Python – Python is easy-to-learn programming language used for exploring and analyzing data. It has special tools like Pandas and Matplotlib that help you clean data and make charts. With Python, you can write short programs to find patterns or answers in data. Its great for tasks that need speed, automation or custom analysis. Even beginners can start using it quickly.
R Programming – R is programming language designed specifically for data analysis and statistics. It helps you handle large datasets, perform detailed analysis and create professional charts. R is often used in research, academic and business environments to understand complex data. It has many built-in functions and packages that make statistical work easier. If you’re interested in numbers and data science, R is a great tool to learn.

Must-Know Frameworks for Every Data Analyst Know

Matplotlib – Matplotlib is powerful Python framework used for creating charts and graphs. It helps data analysts turn numbers into visuals such as line charts, bar graphs and scatter plots. With Matplotlib, you can customize the colors, labels and styles to make your data easier to understand. It’s widely used in reports and presentations to show insights clearly. If you want to explain data in a visual way, this tool is a great start.
Plotly – Plotly is a modern visualization tool that helps create interactive graphs and dashboards. It works with Python, R and JavaScript, allowing users to explore data by hovering, zooming or clicking. Unlike static charts, Plotly visuals respond to user actions, making them perfect for web-based reports. Its used by analysts to build dashboards that are both beautiful and functional.
Excel Power Query – Power Query is a feature inside Microsoft Excel that helps clean and shape data easily. It lets you pull in data from different sources, remove errors and organize it before analysis. You don’t need coding skills just follow step-by-step options to filter and transform the data.
Google Data Studio – Google Data Studio is free tool for turning raw data into live dashboards and visual reports. It connects to Google Sheets, BigQuery and other sources to display updated data in real time. Analysts can build reports using drag-and-drop elements like charts and filters. Its great for sharing insights with clients or teams who want clear and interactive reports.
RapidMiner – RapidMiner is data analytics framework that allows users to build workflows without writing code. You can drag and drop different steps like data loading, cleaning and model building into one process. Its great for freshers because it shows how analytics works step by step. RapidMiner also supports machine learning for tasks like prediction and classification.

Top Skills You Will Master in Data Analytics Training

Understanding Data Structures – You’ll learn how different types of data are stored, organized and accessed in tools like Excel, databases and programming languages. Knowing how data is structured helps you choose the best way to analyze and process it. Whether it’s tables, rows, or columns, understanding the basics helps you work faster and more accurately.
Querying with SQL – SQL is used to find the exact data you need from large databases. You’ll learn how to write simple commands to pull, filter and sort data quickly. This helps you answer questions and create custom data reports. It’s a must-have skill for data analysts in every industry. Even with no coding background, you will find SQL easy to pick up and powerful to use.
Working with Business Dashboards – You’ll learn to create dashboards that show key metrics like sales, performance or user activity in a simple visual format. Dashboards make it easier to track business results and share insights with others. You’ll use tools like Power BI or Tableau to design reports that update automatically. This skill helps you turn raw data into useful summaries.
Interpreting Trends and Patterns – A major part of analytics is spotting what the data is trying to tell you. You’ll learn how to look at numbers and recognize if something is growing, shrinking or staying the same. Understanding patterns helps you predict future outcomes and explain changes. This is useful for everything from marketing campaigns to inventory planning. It is helps you become a more strategic thinker.
Communicating Insights Clearly – Being able to explain what the data means is just as important as finding it. You’ll learn how to present your findings using simple words, charts and summaries. This helps non-technical people understand the value of your analysis. Whether it’s a report or a meeting, clear communication builds trust and drives action.

Exploring Roles and Responsibility in Data Analytics Course

Data Quality Analyst – A Data Quality Analyst ensures that the data used in analysis is accurate, complete and reliable. They check for duplicates, missing values or incorrect entries in large datasets. This role is important because poor data quality can lead to wrong conclusions. They often work with data engineers and analysts to fix data issues at the source. Their job is to maintain high data standards across the organization.
Data Reporting Specialist – This role focuses on building reports that summarize business performance using tools like Power BI or Excel. A Data Reporting Specialist updates dashboards, tracks key numbers and sends regular updates to teams. They make sure reports are clear, accurate and delivered on time. They also work with managers to understand what data is needed. Their main responsibility is to make business data easy to understand.
Marketing Data Analyst – A Marketing Data Analyst helps companies understand their customers and improve marketing efforts. They study website visits, customer behavior and campaign results using analytics tools. Their goal is to find what attracts or loses customers. They create reports showing which ads or messages work best. This role blends creativity with data skills to guide better marketing decisions.
Operations Analyst – Operations Analysts study how daily business activities can be improved using data. They look at things like delivery times, supply usage or staffing levels. By analyzing patterns, they suggest ways to save time or reduce costs. They often use dashboards and reports to explain their findings Their job is to make sure everything runs as smoothly and efficiently as possible.
Product Data Analyst – A Product Data Analyst focuses on how users interact with a company’s product or app. They track user clicks, usage patterns and feature performance to understand what people like or avoid. This role helps product teams decide what changes to make or features to build. They work closely with designers, developers and managers. The goal is to use data to create better user experiences.

Exploring Career Growth for Freshers with Data Analytics

Fast-Track Entry into the IT Field – Data analytics offers a quick way for freshers to step into the tech industry without needing deep programming knowledge. This open the door to IT careers even if you are from a non-technical background. Its an ideal choice for beginners who want to grow fast.
Skill-Based Hiring over Degrees – In data analytics, employers care more about what you can do than your academic background. With practical skills like Excel, Power BI and basic data interpretation, you can stand out. Even freshers with short-term training can land good roles. Its a field where skills truly speak louder than degrees.
Growing Need for Decision Support – Businesses need fresh minds to analyze data and guide decisions in real-time. As a fresher you can help teams understand trends and make smart moves. Your input can shape marketing, sales or customer service strategies. That kind of influence makes the role valuable and exciting from day one.
Wide Scope of Career Paths – Data analytics opens doors to many roles like business analyst, data engineer, or even AI-related jobs. You can explore different domains such as e-commerce, logistics, finance and more. With just one course, you unlock several career directions. This flexibility is perfect for freshers still exploring their interests.
Easy to Start with Online Resources – You don’t need to invest in expensive degrees to learn data analytics. Plenty of affordable or even free courses online can help you get started. With consistent effort you can quickly gain practical skills and land a job. It’s one of the most accessible and rewarding career choices for beginners today.

How Data Analytics Skills Help You Land Remote Jobs

Work from Anywhere – Most data work happens on a computer, so you don’t need to be in a physical office. With the right tools and internet, you can clean, analyze and share data from home. This makes data analytics one of the easiest fields to work in remotely. Companies just want results, not your location. As long as you can deliver, you can work from anywhere.
Remote-Friendly Roles – Today, businesses want to cut costs by hiring remote workers instead of maintaining big offices. Data analytics roles are perfect for this model because they’re digital by nature. If you show you can work independently and meet deadlines, employers will trust you remotely. Your skills become your biggest asset, not your location. This opens up more remote job chances for you.
Online Portfolio Advantage – With data analytics, you can create sample projects and publish them on sites like GitHub or your personal blog. This helps show your skills to companies looking for remote talent. You don’t need in-person interviews if your work speaks for you online. A good portfolio builds trust with hiring managers around the world. It’s a strong way to get noticed for remote roles.
Cloud-Based Tools – Most analytics tools like Excel, SQL and Tableau are cloud-based or can be shared online. You can collaborate with teams, attend virtual meetings and send dashboards without needing to be on-site. This makes it easy for employers to hire analysts who can work from different locations. Your ability to use these tools effectively proves you're remote-ready. That’s a big plus in today job market.
Global Work Access – Once you learn data analytics, you're not limited to jobs in your city or country. Freelance sites and global companies offer remote positions to skilled professionals anywhere. You can earn in different currencies and work with clients across time zones. This global exposure also helps you learn new things faster. Its a great way to grow both professionally and personally from home.

What to Expect in Your First Data Analytics Job

Hands-On Data Tasks – In your first job, expect to spend a lot of time collecting, organizing and cleaning raw data. The information may come from various sources and will often need fixing before analysis. You will be handling spreadsheets, databases or dashboards daily. This stage helps you understand the importance of accuracy. Its the first step to uncovering valuable insights.
Learning the Company’s Way – Every organization handles data differently, so you'll need to adjust to their methods. You might use custom software, internal dashboards or unique file formats. Its normal to feel confused at first, but training and support will be provided. Try to absorb as much as you can in the early days. The quicker you adapt the more confident you'll feel.
Explaining Data to Others – You will often need to share your findings with people who don’t understand analytics. Whether its a chart or a simple report, your job is to make the message clear. Being able to explain numbers in a simple way is a valuable skill. Clear communication helps your insights make a real impact.
Contributing to Business Goals – Your work will help teams solve actual business problems, like improving customer service or cutting costs. You might be asked to spot patterns, compare trends or test a new idea with data. These tasks make you feel like part of something important. Its rewarding to know your analysis supports better decisions. Each project is a new challenge that helps you grow.
Growing with Every Project – You will quickly discover that learning doesn’t stop after training. New tools, techniques or data challenges will keep coming your way. From automation to predictive models, there’s always more to explore. Prepare yourself to take notes, pose inquiries and try things independently. Every project adds to your confidence and skill set.

Top Company Hiring for Data Analytics Professionals

TCS (Tata Consultancy Services) – Tata Consultancy Services is one of India’s popular IT service companies, offering data analytics roles across various sectors. Data professionals here work on data processing, business reports and automation tasks for global clients. TCS provides strong mentorship, upskilling programs and clear career paths for freshers. It’s a great starting point for building a long-term career in analytics.
Wipro – Wipro is a major tech company that uses data analytics to support business transformation across industries like banking, healthcare and telecom. Data analysts here focus on data cleansing, building reports and analyzing trends. Wipro offers structured learning, certifications and exposure to advanced tools. Its a supportive environment for beginners to grow and gain project experience.
Capgemini – Capgemini is global consulting and technology firm hiring data professionals for analytics, cloud and AI-related roles. Analysts at Capgemini work on solving business problems through dashboards, forecasting and data storytelling. The company offers a collaborative culture and continuous training. Its a solid choice for those who want international project exposure and skill building opportunities.
Genpact – Genpact is known for helping companies improve their processes using data analytics and automation. Analysts here work on real-time use cases in finance, supply chain and customer experience. The company encourages practical thinking and rewards problem-solving abilities. It’s beginner-friendly, offering hands-on experience with clients and internal projects.
Accenture – Accenture is global leader in the consulting and technology, with a strong focus on data-driven transformation. They hire data analysts to handle big data, business intelligence and predictive models across industries. Freshers receive training in tools like Power BI, Python and SQL. Its a dynamic workplace for learning, innovation and cross domain analytics.

Upcoming Batches For Classroom and Online

Weekdays

28 - July - 2025

08:00 AM & 10:00 AM

Weekdays

30 - July - 2025

08:00 AM & 10:00 AM

Weekends

02 - Aug - 2025

(10:00 AM - 01:30 PM)

Weekends

03 - Aug - 2025

(09:00 AM - 02:00 PM)

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Who Should Take a Data Analytics Training

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Working Professionals

Diploma Holders

Professionals from Other Fields

Salary Hike

Graduates with Less Than 60%

Job Roles For Data Analytics Course

Data Analyst

Business Analyst

Data Scientist

Analytics Consultant

BI Developer

Data Engineer

Statistician Analyst

Data Quality Analyst

Tools Covered For Data Analytics Training

What’s included ?

📊 Free 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.

🛠️ Hands-On Projects

Work on real-time projects to apply what you learn.
Build mini apps and tools daily to enhance your coding skills.
Gain practical experience just like in real jobs.

🧠 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.

🎯 Interview Preparation For Freshers

Practice company-based interview questions.
Take online assessment tests to crack interviews
Practice confidently with real-world interview and project-based questions.

🧪 LMS Online Learning Platform

Explore expert trainer videos and documents to boost your learning.
Study anytime with on-demand videos and detailed documents.
Quickly find topics with organized learning materials.

Data Analytics Course Syllabus

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

Our Data Analytics Course in Anna Nagar is designed with flexible learning options to align with your career goals. The training covers essential tools and topics such as Excel, SQL, Python, Power BI and foundational Machine Learning concepts. You’ll gain hands-on experience through Data Analytics Internships involving real-time projects. After completing the Data Analytics Course in Offline, you’ll receive an industry-recognized certificate that showcases your skills. We also provide strong Data Analytics Placement support to help you launch your analytics career. Join our expert-led Data Analytics Training in Anna Nagar and step confidently into the data-driven future.

Data Analytics with Python – Learn Python programming along with popular libraries like Pandas, NumPy and Matplotlib to efficiently clean, analyze and visualize data.
Data Analytics with R – Dive into R programming to perform data analysis, statistical modeling and create advanced visualizations.
Business Analytics Track – Master practical tools such as Excel, Power BI and SQL to analyze business data, build interactive dashboards.
Machine Learning Track – Gain practical experience with machine learning algorithms, data preprocessing 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, 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
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
Correlation & Regression – Relationships and prediction between variables

🎁 Free Addon Programs

Aptitude, Spoken English

🎯 Our Placement Activities

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

Get Real-Time Experience in Data Analytics Projects

Project 1

Sales Forecasting

In this project, you’ll predict future sales using past data trends. Python tools like Pandas, NumPy and Scikit-learn are used along with ARIMA or regression models.

Project 2

Data Visualization

This project helps you turn data into visual stories using Power BI, Tableau or Python libraries. You’ll create dashboards, graphs and charts to present insights clearly.

Project 3

Time Series Analysis

Work with time-based data to identify trends and make predictions. Python tools like Statsmodels and Matplotlib are used to apply techniques like moving averages and ARIMA.

Project 4

Stock Prices Predictor

Predict stock trends using real-time financial data and Python libraries like yfinance and Scikit-learn. You will apply models like Linear Regression or LSTM for better accuracy.

Project 5

Data Visualization

Create an interactive bank loan dashboard using Tableau or Power BI to visualize loan approvals, customer demographics, and default rates with dynamic filters.

Placement Support Overview

Today's Top Job Openings for Data Analytics

Service Desk Analyst

Company Code : WPO416

Chennai, Tamilnadu

₹30,000 - ₹40,000 a month

Any Degree

Exp 0-2 yrs

We are looking for fresh graduates with excellent communication skills in English and strong technical knowledge to join our team as Support Analysts. Candidates must hold a minimum graduation degree with all marksheets available. In this role, you will assist users by diagnosing and resolving IT issues.

Easy Apply

Business Analyst

Company Code : CPS805

Chennai, Tamilnadu

₹30,000 - ₹40,000 a month

Any Degree

Exp 0-1yr

We are currently recruiting for dynamic Business Analysts will drive our continued growth and success. With a strong focus on innovation, they will identify, develop and support the implementation of strategic initiatives to enhance efficiency and productivity.

Easy Apply

Data Scientist

Company Code : CFD612

Chennai, Tamilnadu

₹20,000 - ₹40,000 a month

Any Degree

Exp 0-5 yrs

In this role you will analyze complex data sets, develop predictive models and generate actionable insights to support business strategies. The role requires strong expertise in statistics, machine learning and data visualization.

Easy Apply

Data Engineer

Company Code : YST413

Chennai, Tamilnadu

₹15,000 - ₹25,000 a month

Any Degree

Exp 0-3 yrs

Opportunities are now open for a data analytics professional with hands-on experience in big data tools such as Hadoop, Spark and Kafka, along with strong proficiency in scripting languages like Python and Scala. The ideal candidate should have expertise in both SQL and NoSQL databases, including Postgres and MongoDB and be capable of building scalable data pipelines and architectures.

Easy Apply

Marketing Analyst

Company Code : MPN316

Chennai, Tamilnadu

₹25,000 - ₹50,000 a month

Any Degree

Exp 0-3 yrs

We are expanding and hiring for a Marketing Analyst can develop and implement effective marketing analysis solutions to support organizational goals. The role involves monitoring key performance metrics, conducting detailed analysis and preparing reports to guide decision-making. Collaboration with cross-functional teams and the creation of technical documentation are also key responsibilities.

Easy Apply

Operations Analyst

Company Code : CRL431

Chennai, Tamilnadu

₹30,000 - ₹50,000 a month

Any Degree

Exp 0-1 yrs

We are actively seeking qualified candidates for a detail-oriented professional to manage accruals, invoice processing and contract documentation in close coordination with business and internal teams. The role involves regular interaction with Finance, Compliance, Tax and IT/Infosec departments, ensuring smooth operations and timely resolution of queries. Responsibilities also include preparing MIS reports.

Easy Apply

Business Analyst

Company Code : ZKA721

Chennai, Tamilnadu

₹25,000 - ₹50,000 a month

Any Degree

Exp 0-2 yrs

Join our team – we are hiring talented people to become part of our team, where salary will not be a constraint for deserving candidates. The role involves reviewing and analyzing current systems to evaluate their efficiency and effectiveness, while recommending strategic improvements. Candidates will also define the scope and parameters of analysis to ensure measurable outcomes and actionable results.

Easy Apply

Data Analyst

Company Code : WSA812

Chennai, Tamilnadu

₹15,000 - ₹25,000 a month

Any Degree

Exp 0-1 yrs

Exciting roles available – apply now For Data Engineer/Analyst to join our team at We Shine Academic and support the development of scalable data infrastructure. The role involves collaborating with teams to gather requirements, building and optimizing data systems and preparing raw data for analysis. Candidates should have a strong focus on accuracy.

Easy Apply

Highlights for Data Analytics Internship in Anna Nagar

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.

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Skill Development Workshops

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

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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.

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Mentorship & Peer Learning

1. Learn under experienced mentor guide your technical and career growth.
2. Collaborate with peers to enhance learning through code reviews and group projects.

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Soft Skills & Career Readiness

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

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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.

0 to 1 yr 0 to 2 yrs 0 to 3 yrs Non IT To IT Career Gap to IT Diploma to IT

Company-Specific Interview Questions from Top MNCs

1. What is Data Science and how is it different from Data Analytics?

Ans:

Data Science involves building models and using algorithms to predict future outcomes. Data Analytics is more about analyzing past data to find useful insights and trends.

2. What does a Data Scientist do in a company?

Ans:

A Data Scientist gathers, cleans and studies data to help the company solve problems, improve services and make better decisions.

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

Ans:

Similarly to spreadsheets, structured data is organized neatly in rows and columns. Unstructured data includes things like emails, images and videos, which don’t follow a fixed format.

4. What are the main steps in a Data Science project?

Ans:

Typical steps are defining the problem, collecting data, cleaning it, analyzing it, building models and sharing insights or results.

5. How should a dataset with missing data be handled?

Ans:

You can either fill missing values using average or median, remove the rows, or predict them using other available data, depending on the case.

6. How does supervised learning differ from unsupervised learning?

Ans:

Supervised learning uses data with known answers to train models. Unsupervised learning finds hidden patterns in data without any labels.

7. What is cross-validation in machine learning?

Ans:

Cross-validation is a way to test how well your model works by dividing the data into smaller sets and checking accuracy on each set.

8. What does a confusion matrix show?

Ans:

A confusion matrix shows how well your classification model is performing by comparing actual results with predicted ones, including correct and incorrect outcomes.

9. How do you select important features in a dataset?

Ans:

You may use strategies like Recursive Feature Elimination (RFE), feature importance scores from models and correlation analysis.

10. How does the K-Nearest Neighbors (KNN) algorithm work?

Ans:

KNN compares a new data point with the 'k' closest known points and predicts its value based on the majority of those neighbors.

1. What does a Data Analyst do?

Ans:

A Data Analyst collects and studies data to help companies make better decisions by identifying trends, patterns and useful business insights.

2. How do you maintain data quality during analysis?

Ans:

To ensure good data quality, you must verify sources, correct errors, remove duplicates and double-check the results for accuracy.

3. What is data cleaning and why is it important?

Ans:

Data cleaning means fixing or removing wrong, missing, or duplicate data. It’s important because clean data leads to accurate and reliable analysis.

4. Which tools are used for data analysis?

Ans:

Tools like Excel for spreadsheets, SQL for querying databases, Python for scripting, Power BI and Tableau for visual reports are used.

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

Ans:

Every row in a table is uniquely identified by its main key. A foreign key links two tables by referring to the primary key of another table.

6. How do you handle missing or incomplete data?

Ans:

You can fill missing values with averages, remove incomplete rows or use prediction methods depending on the situation and dataset size.

7. What is data normalization in databases?

Ans:

Normalization is the process of structuring data to reduce duplication and improve consistency by organizing it into related tables.

8. What is a pivot table in Excel and how is it useful?

Ans:

A pivot table helps summarize large data sets by grouping, sorting and calculating totals or averages to make data easier to analyze.

9. What is the difference between correlation and causation?

Ans:

Correlation shows that two things move together, while causation means one directly affects the other. Not all correlations are causal.

10. Why is data visualization important?

Ans:

Charts and graphs are used in data visualization to assist users quickly see trends and insights in complex data.

1. What tools are commonly used in data analysis?

Ans:

Data analysts often use tools like Excel for basic analysis, SQL for managing and querying databases, Tableau and Power BI for creating interactive dashboards and Python or R for advanced data manipulation and automation.

2. How to deal with missing values in a dataset?

Ans:

Depending on the situation, there are various approaches to handling missing data. You can remove incomplete rows, fill missing values with mean, median or mode, or use more advanced imputation techniques such as regression or machine learning predictions.

3. What are the types of joins in SQL?

Ans:

INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN

4. How is a database different from a data warehouse?

Ans:

A database is designed to store and manage current transactional data, supporting day-to-day operations. In contrast, a data warehouse stores large volumes of historical data optimized for analytical queries and business reporting.

5. Why is data cleaning important in analysis?

Ans:

Data cleaning ensures the dataset is accurate, consistent and free of errors or duplicates. Clean data improves the quality of analysis, making insights more reliable and actionable for business decisions.

6. What does data normalization mean and why is it important?

Ans:

Data normalization is the process of organizing data to increase uniformity and decrease redundancy. It simplifies data management, enhances data integrity and optimizes storage in relational databases.

7. How is data validation performed?

Ans:

By checking the format, range and consistency of data inputs
Ensuring values follow set rules (e.g., dates, numbers, text)
Using built-in validation functions in Excel or programming scripts

8. What is data visualization and its purpose?

Ans:

Data visualization uses graphs, charts and plots to represent data in a visual format. It helps communicate complex information clearly and quickly, making patterns, trends and insights easier to understand.

9. What is data modeling in simple terms?

Ans:

Data modeling is the process of designing how data is stored and related within a system. It involves creating diagrams or structures that define tables, fields and relationships to support efficient data storage and retrieval.

10. How to create a Pivot Table in Excel?

Ans:

Select your data range
Select the toolbar's "Insert" tab
Choose “Pivot Table” from the menu options
Data analysis involves dragging and dropping fields into rows, columns, and values sections.

1. Which tools are commonly used for analyzing data?

Ans:

Tools like Excel, SQL, Tableau, Power BI and Google Sheets are frequently used for organizing, analyzing and visualizing data effectively across business functions.

2. What is a pivot table and how is it useful?

Ans:

Summarizes large datasets in a compact form
Helps in grouping, filtering and comparing data
Allows for quick insights and dynamic reports

3. What is the best way to manage missing values in a dataset?

Ans:

Missing values can be handled by removing incomplete entries, filling gaps with averages or medians, or using predictive techniques such as data imputation.

4. How can supervised and unsupervised learning be explained?

Ans:

Supervised learning involves training models using labeled datasets for prediction, whereas unsupervised learning identifies patterns and groupings from unlabeled data.

5. Why is the p-value important in statistics?

Ans:

Measures the significance of test results
Helps decide whether to accept or reject a hypothesis
Indicates the likelihood of results occurring by chance

6. Can a challenging data project be described?

Ans:

Dealing with unstructured, messy data
Extracting meaningful patterns using analytical tools
Turning insights into actionable business recommendations

7. What does regression analysis mean?

Ans:

A statistical technique for determining relationship between independent and dependent variables, regression analysis is frequently used to forecast or make predictions.

8. What are the types of regression used in analysis?

Ans:

Common types include linear regression for straight-line prediction, logistic regression for binary classification and ridge or polynomial regression for more complex relationships.

9. How can data quality be maintained?

Ans:

Maintaining data quality involves validating sources, ensuring consistency, removing duplicates and keeping data accurate, complete and up-to-date.

10. What is meant by data normalization and why is it useful?

Ans:

Data normalization is the process of organizing data to minimize redundancy and improve integrity, making databases more efficient and easier to manage.

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

Ans:

Using reports and dashboards a data analyst is in charge of gathering, cleaning and analyzing data in order to spot patterns, derive insights and assist companies in making defensible decisions.

2. How is data analysis different from data science?

Ans:

Data analysis focuses on interpreting historical data to understand past trends, while data science uses advanced algorithms and models to predict future outcomes and provide deeper insights.

3. What part does a data analyst play in a project team?

Ans:

Converts raw data into actionable insights
Supports decision-making through clear reports and visualizations
Collaborates with stakeholders to define business needs

4. Why is making decisions based on data important?

Ans:

Reduces reliance on guesswork
Supports more objective and confident choices
Improves business outcomes with fact-based actions

5. Which tools are often used for analyzing data?

Ans:

Common tools include Excel, Power BI, Tableau, SQL and Google Sheets. These tools help analyze data by organizing, filtering and visualizing it to support clear interpretation.

6. What is SQL and how is it helpful in data work?

Ans:

Database management and querying are done via SQL (Structured Query Language). It enables analysts to efficiently retrieve and manipulate data stored in relational databases.

7. What is meant by a primary key in a database?

Ans:

A primary key is a special number that is linked to every table row. It ensures that no two rows contain the same key, helping maintain data integrity and avoid redundancy.

8. What are the usual types of data formats seen in analytics?

Ans:

Analysts commonly work with formats like CSV, Excel spreadsheets, JSON and SQL tables. These formats make it easier to store, transfer and process data across various tools and systems.

9. How is good data quality described and checked?

Ans:

Good quality data is accurate, complete, consistent and timely. Analysts check data quality by identifying missing values, removing duplicates and correcting inconsistencies.

10. How can data be kept accurate and trustworthy?

Ans:

By validating data sources, applying data cleaning steps, checking for inconsistencies, performing spot checks, and documenting all transformations and assumptions.

Perform routine data validations and audits
Clean and update data regularly
Rely on verified and consistent data sources

Disclaimer Note:

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

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Getting Started With Data Analytics Course in Chennai

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 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.

📜 Industry-Recognized Certification

Earn official, shareable certifications recognized by top companies — a powerful proof of your job-ready skills.

🚀 Get Discovered by Top Employers

Let recruiters come to you with your verified profile — focus on learning while we connect you to real opportunities.

💼 Real-World Project Experience

Work on real-world projects to sharpen your skills and build a portfolio that impresses employers instantly.

What certifications are included in the Data Analytics program?

You’ll have the chance to earn top industry certifications such as:

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

Does Data Analytics Certification guarantee a job?

It guarantees a job, it greatly improves your chances. It shows you have the skills, dedication and practical training needed making you a strong candidate during hiring.

How long does it take to get certified?

Most certifications take 3 to 6 months depending on your learning speed. With focused study, you can complete it even faster.

Why should I get certified in Data Analytics?

Certifications help you stand out, prove your skills and increase your job opportunities and earning potential. They also give you more confidence when applying for roles.

How can I prepare for certification exams?

Join a well-structured training program, work on real-time projects, practice tools like Excel, SQL, Python and take mock exams. Study groups and online communities can also help a lot.

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 Course Fees in Chennai

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

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Feature

ACTE Technologies

Other Institutes

Affordable Fees

Competitive Pricing With Flexible Payment Options.

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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.

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Placement Support

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

Basic Placement Support

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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.

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

Limited Mentor Support and No After-hours Assistance.

Data Analytics Course FAQs

1. What are the requirements for becoming a Data Analyst?

To become a Data Analyst, you typically need a basic understanding of the statistics, mathematics and computer skills. It's crucial to have knowledge of at least one computer language, such as Python or R, and tools like Excel and SQL.

2. How promising is the future for Data Analysts?

The future for Data Analysts is highly promising as data continues to drive decisions across all industries. Companies rely on data insights for marketing, finance, healthcare and more. With increasing demand for data-driven strategies, skilled data analysts will remain in high demand with strong job growth and salary potential.

3. What technologies are covered in the Data Analytics Training?

The training typically covers core technologies such as Excel, SQL, Python, R, Tableau, Power BI and data visualization tools. You’ll also learn data wrangling, statistical analysis, predictive modeling and hands-on experience with real datasets using industry-standard tools.

4. Are there real-time projects included in the Data Analytics Training?

In actual fact, the training includes practical, industry-based projects that let you put the knowledge you've gained to use. These projects simulate real business problems and help you gain hands-on experience, preparing you for real-world job roles.

5. Do you provide any resume-building supports and grooming classes?

Yes, we offer complete resume-building support and grooming classes as part of the training. Our experts help you craft professional resume tailored to data analytics roles, highlight your skills and projects and provide tips to improve your job search and interview success.

1. Who can become a Data Analytics professional?

Anyone with an interest in data, problem-solving and logical thinking can pursue a career in Data Analytics. Whether you're a student, working professional, or someone looking to switch careers, you can start learning data analytics with the right training and practice.

2. Do I need a degree to become a Data Analyst?

A degree is not always required to become a Data Analyst. Many professionals enter the field through certification programs and practical experience. However, having a degree in fields like mathematics, statistics, computer science, or business can be an added advantage.

3. What skills should I have before joining a Data Analytics course?

Before joining a Data Analytics course, basic knowledge of Excel, logical thinking and a comfort with numbers are helpful. While not mandatory, familiarity with programming (like Python) or statistics can make learning smoother and more effective.

4. Do I need to know both frontend and backend before joining a course?

No, you don’t need frontend or backend knowledge to start a Data Analytics course. These are more relevant to web development. Data Analytics focuses on understanding, processing and visualizing data using tools like SQL, Excel, Power BI, or Python.

1. What kind of placement support is provided after the Data Analytics Training?

After completing the Data Analytics training, you will receive complete placement support, including resume building, mock interviews, job referrals and interview scheduling with partner companies to boost your chances of getting hired.

2. Will I get access to real-time projects for my resume?

Yes, you will work on real-time industry projects during the course which can be added to your resume. These hands-on projects help you gain practical skills and showcase your expertise to potential employers.

3. Can I apply for jobs top IT companies after completing the course?

Absolutely, With the skills and certification gained from the training, you can apply for roles in leading IT companies and analytics firms. Many of our learners have successfully secured positions in MNCs after course completion.

4. Is placement available for freshers with no experience in data analytics?

Yes, dedicated placement support is available for freshers as well. Guidance is provided to help you build your portfolio, prepare for entry-level interviews and connect with recruiters hiring fresh graduates.

1. Do I get a certificate after finishing the Data Analytics course?

Yes, upon completing the Data Analytics training, you will be rewarded a certificate of course completion. You can improve your career prospects by adding this certificate to your LinkedIn profile or CV, which attests to your study.

2. Is it a good idea to learn Data Analytics?

Absolutely. Learning Data Analytics is a smart move as it opens doors to high-demand jobs across industries. Companies rely heavily on data to make smart decisions, making skilled analysts valuable assets.

3. What should I know before joining a Data Analytics course?

Before joining, it’s helpful to have basic computer skills, logical thinking and a little understanding of math or statistics. While not mandatory, familiarity with Excel or any programming language can be an added advantage.

4. How does a Data Analytics course help my career?

A Data Analytics course gives you the practical skills to work with real-world data. It increases your employability, helps you stand out in job interviews and opens opportunities in various sectors like finance, healthcare, marketing and IT.

5. What skills will I learn in the Data Analytics course?

Using programs like Excel, SQL, Python and Power BI, you will discover how to gather, clean, and evaluate data. You'll also gain skills in data visualization, statistical analysis and interpreting insights to support business decisions.

1. Will I Get Support for Placement support After the Course?

Yes, most Data Analytics training programs provide placement assistance, including resume building, interview preparation and job referrals to help students start their careers successfully.

2. Why do fees differ between training centers?

Fees may vary based on the institute’s reputation, trainer experience, infrastructure and additional services like mentorship or placement support. City-wise cost of living can also impact pricing.

3. Is the course reasonably priced for beginners?

Yes, the course is designed to be affordable for beginners, offering great value through hands-on training, project work and expert guidance at a beginner friendly cost.