Best Data Analytics Training in Hyderabad with 100% Placement
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Data Analytics Training in Hyderabad

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24-Jun-2024
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Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

19-Jun-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

22-Jun-2024
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(10:00 AM - 01:30 PM)

(Class 3hr - 3:30Hrs) / Per Session

22-Jun-2024
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(Class 4:30Hr - 5:00Hrs) / Per Session

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Gain the Best Practices In Data Analytics Course

  • The goal of our data analytics course is to give students the skill sets and understanding required to analyze data and derive valuable knowledge for decision-making.
  • These courses will be useful for beginners and professionals wishing to progress in data science, business intelligence, and related domains.
  • Our data Analytics course curriculum covers essential topics, progressing from basic subjects like programming and statistics to more complex ones like machine learning and big data analytics.
  • Students can demonstrate their skills at the end of the course by working on a large-scale data analytics project as part of a capstone project, frequently in partnership with business partners.
  • Our trainers are typically experts in the field with advanced degrees in data science and analytics, as well as substantial experience. They have real-world experience in a variety of industries.
  • Our Data Analytics course provides comprehensive placement support, including resume building, interview preparation, and job placement assistance. It also offers career counselling, job fairs, industry connections, and alumni networking opportunities.
  • After finishing, our students typically obtain certification accepted by the industry. To increase their employability, we also help students get ready for additional certifications from reputable organizations like Google, Microsoft, or SAS.
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

Course Objectives

Absolutely, there is a need for data analytics training. It gives people the tools they need to solve challenging problems, make wise decisions, and analyze data. Such training is highly valuable because businesses in today's data-driven world depend on data analytics to obtain insights, enhance performance, and maintain their competitiveness.
  • Familiarity with fundamental data analysis concepts
  • A basic knowledge of statistics and mathematics
  • knowledge of data manipulation tools such as SQL
  • An understanding of programming languages such as R or Python
  • Increased Career Opportunities
  • Improved Analytical Skills
  • A competitive advantage
  • Increased Earning Potential
  • Ability to Adjust to Technological Developments
Actually, the purpose of data analytics training courses is to help rookies learn everything from scratch. To make it accessible to those who are new to the field, they begin with basic concepts, offer step-by-step guidance, and incorporate practical exercises to ensure gradual understanding and skill development.
  • Data Analyst
  • Business Analyst
  • Database Administrator
  • Data Scientist
  • Data Engineer
  • Business Intelligence Analyst
  • Machine Learning Engineer
  • Data visualization techniques using tools like Tableau or Power BI
  • Data cleaning and preprocessing
  • Machine learning algorithms for predictive modelling
  • Database management and querying with SQL
  • Big data technologies like Hadoop and Spark
  • Ethical considerations and data governance
Data analytics can be difficult because handling massive volumes of data, comprehending statistical concepts, and becoming proficient with a variety of tools and techniques is often hard. But with commitment, training, and practice, people can overcome these obstacles and acquire the abilities required to succeed in the industry.

Is Coding Necessary for Data Analytics?

Yes, coding is required for data analytics tasks, particularly for cleaning, analyzing, and modelling data. Expertise in programming languages such as R or Python enables analysts to generate unique solutions, automate procedures, and work with data effectively, thereby augmenting their capacity to extract insights and make well-informed decisions.

What hands-on experience is offered in the Data Analytics training?

  • Programming Assignments
  • Data Cleaning and Preprocessing
  • Machine Learning Implementation
  • Database Management Tasks
  • Data Visualization Exercises
  • Practical Data Analysis Projects

Is Data Analytics an IT skill?

Actually, since data analytics entails using software and technology to analyze and interpret data, it is an IT skill. Data extraction is a crucial component of IT expertise, with professionals in this field using database management systems, programming languages, and a range of analytical tools to extract insights from data.

Where is Data Analytics utilized?

  • Business
  • Finance
  • Marketing and Advertising
  • Healthcare
  • Manufacturing
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An Detailed Overview of Data Analytics

Analyzing enormous amounts of data to find patterns, trends, and insights that can guide decision-making is known as data analytics. To extract useful information from data, it makes use of methods like statistical analysis, machine learning, and data visualization. Put, it aids businesses and other organizations in better understanding their data, which encourages them to solve issues, find growth prospects, and make better decisions. In today's data-driven world, data analytics is used in a variety of industries, such as marketing, finance, healthcare, and retail, to streamline operations, increase productivity, and gain an edge over their competitors.

 

Additional Info

Mastering the Essentials Key Concepts and Techniques in Data Analytics

  • Data Preprocessing: This entails preparing the data for analysis by cleaning, transforming, and organizing it. Some examples of tasks are managing missing values, eliminating duplicates, and standardizing data formats.
  • Descriptive Analytics: The goal of descriptive analytics is to comprehend historical trends and patterns by compiling and analyzing data. Methods like summary statistics and data visualization are frequently applied here.
  • Exploratory Data Analysis (EDA): To find connections, patterns, and anomalies in data, EDA entails visualizing and analyzing the data. Correlation analysis, histograms, and scatter plots are among the methods.
  • Inferential Statistics: This is using a sample of data to draw conclusions and forecasts about the population. This group includes methods like regression analysis and hypothesis testing.
  • Predictive Analytics: Predictive analytics utilizes past data to make predictions about future patterns and actions. Predictive modelling frequently uses machine learning algorithms like neural networks, decision trees, and regression.
  • Prescriptive Analytics: It entails making recommendations for decisions or actions based on optimization methods and predictive models. It seeks to determine the most effective path to achieving a desired goal.
  • Machine Learning: Without explicit programming, machine learning algorithms use data to learn and make predictions or decisions. Common machine-learning techniques include reinforcement learning, supervised learning, and unsupervised learning.
  • Big Data Analytics: Large and complex datasets that are inefficiently handled by traditional data processing applications are the focus of big data analytics. Big data processing is frequently done with technologies like Spark and Hadoop.
  • Natural Language Processing (NLP): NLP involves analyzing and interpreting data related to human language. Methods like text classification, named entity recognition, and sentiment analysis are frequently used in NLP.
  • Time Series Analysis: The goal of time series analysis is to find patterns, trends, and seasonality in data gathered over time. Time series analysis frequently uses methods like moving averages, exponential smoothing, and ARIMA modeling.
  • Data Visualization: The graphical depiction of data used to communicate insights effectively is known as data visualization. Graphs, charts, and interactive dashboards are examples of techniques.
  • Dimensionality Reduction: Principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) are two dimensionality reduction techniques that are used to reduce the number of variables in a dataset while maintaining crucial information.

Explore The Career Paths in Data Analytics

  • Data Analyst: Data analysts are in charge of gathering, preparing, and evaluating data in order to offer insights and facilitate decision-making. When interpreting data and presenting findings to stakeholders, they frequently use databases, spreadsheets, and data visualization tools.
  • Business Analyst: Business process analysis, opportunity identification, and solution recommendation are the main focuses of business analysts. The goal is to increase profitability and efficiency. They collaborate closely with stakeholders to comprehend business needs and convert them into recommendations and insights based on data.
  • Data Scientist: Data scientists are adept at analyzing complex datasets and deriving useful insights by applying cutting-edge statistical and machine-learning techniques. To address business issues and spur innovation, they create data-driven solutions, predictive models, and algorithms.
  • Machine Learning Engineer: To enable intelligent systems and automate decision-making processes, machine learning engineers create, develop, and implement machine learning models and algorithms. Their areas of expertise include algorithm development, data modelling, and programming. They frequently use scikit-learn, TensorFlow, and Python.
  • Data Engineer: To ensure effective data collection, storage, and processing, data engineers concentrate on developing and maintaining data pipelines and infrastructure. They collaborate with big data technologies, databases, and cloud platforms to create scalable and reliable data architectures that facilitate analytics and decision-making.
  • Quantitative Analyst: Quants model risk in financial markets, analyze financial data and create trading strategies using statistical and mathematical methods. They are employed by investment banks, hedge funds, and other financial institutions, frequently in fields like risk management, algorithmic trading, and quantitative finance.
  • Business Intelligence (BI) Developer: Business intelligence (BI) developers create and implement business intelligence solutions that let organizations see and analyze data to make decisions. For stakeholders, they produce reports, dashboards, and interactive visualizations using BI tools like Tableau, Power BI, and Qlik.
  • Data Visualization Specialist: Experts in data visualization concentrate on developing and producing eye-catching visuals that successfully convey data-driven insights. Their areas of expertise include graphic design, data visualization principles, and using Tableau, D3.js, and Adobe Illustrator to tell stories.
  • Data Architect: An organization's entire data architecture and infrastructure are designed and overseen by data architects. They create data models, establish data standards, and guarantee data security, compliance, and integrity throughout databases and systems.

The Role of Data Analytics in Business Decision-Making

  • Data-Driven Decision Making: Businesses may now base decisions on facts rather than hunches or gut feelings thanks to data analytics. Businesses can make more objective and well-informed decisions by using data analysis to find trends, patterns, and correlations that may not be immediately apparent.
  • Understanding Customer Behavior: Businesses can learn about consumer preferences, purchasing patterns, and demographics by analyzing customer data. Businesses can better meet customer needs and preferences by customizing their products, services, and marketing strategies with the use of this information.
  • Improving Operational Efficiency: Data analytics can improve numerous business operations and processes, including supply chain management, inventory management, and production planning. By analyzing operational data, businesses can reduce costs and boost efficiency by identifying inefficiencies, bottlenecks, and improvement opportunities.
  • Identifying Market Opportunities: Businesses can use data analytics to find new market trends, business opportunities, and threats from competitors. Businesses can take advantage of opportunities and stay ahead of the competition by analyzing market data to obtain insights into consumer demand, competitor strategies, and market dynamics.
  • Risk Management: Through the analysis of past data and the identification of potential risk factors, data analytics can assist businesses in identifying and mitigating risks. Businesses are better equipped to evaluate risk exposure, forecast future events, and take proactive steps to reduce risks when they use predictive analytics and modelling techniques.
  • Personalized Marketing and Customer Experience: Businesses can use data analytics to provide individualized marketing messages and customer experiences based on individual preferences, behavior, and demographics. By analyzing customer data, businesses can segment their target audience, personalize marketing campaigns, and distribute pertinent content through a variety of channels.
  • Optimizing Pricing Strategies: Data analytics can assist businesses in optimizing their pricing strategies by analyzing competitor pricing, market dynamics, and consumer behaviour. By utilizing pricing analytics techniques, businesses can increase overall revenue, maximize profitability, and set optimal prices.
  • Forecasting and Planning: Thanks to data analytics, businesses can predict future trends, demand, and performance metrics by using predictive modelling techniques and historical data. Businesses can also make well-informed decisions about budgeting, strategic planning, and resource allocation by utilizing forecasting and planning tools.
  • Enhancing Product Development: Data analytics can help businesses create new products and enhance their current ones by offering insightful information about consumer feedback, market demand, and product performance. Businesses can find features in their products that customers find appealing, solve customer problems, and spur innovation by analyzing product data.

Popular Tools Used in Data Analytics

  • Python: Python is a flexible programming language that is very popular in the data analytics field because of its abundance of libraries, ease of use, and flexibility. Libraries like pandas, NumPy, SciPy, and sci-kit provide strong tools for data manipulation, analysis, and machine learning.
  • R: R is a programming language and environment created especially for statistical computing and graphics. Because of its large collection of packages for data analysis, visualization, and modeling, statisticians and data scientists find it popular.
  • SQL (Structured Query Language): Relational databases are managed and queried using SQL, a standard language. Data engineers and analysts must be able to effectively extract, transform, and analyze data from databases.
  • Excel: Spreadsheet software like Microsoft Excel is popular because it provides basic data analysis features like pivot tables, charting, and data manipulation. Data visualization and rapid ad hoc analysis are two common uses for it.
  • Tableau: One of the best tools for data visualization is Tableau, which lets users create dynamic, eye-catching dashboards and reports. With drag-and-drop functionality, users can explore and analyze data from various sources.
  • Power BI: Microsoft Power BI is a business analytics tool that offers self-service BI capabilities for data analysis and insight sharing within an organization. It provides tools for collaboration, data preparation, and visualization.
  • Apache Spark: Fast and versatile, Apache Spark is a distributed computing engine built for handling large amounts of data. It offers APIs for creating parallel apps and carrying out sophisticated analytics operations like machine learning and graph processing in a variety of languages, including Scala, Java, and Python.
  • Hadoop: An open-source framework called Apache Hadoop processes and stores massive datasets in a distributed manner across computer clusters. It frequently stores and analyzes big data by utilizing the MapReduce programming model and the Hadoop Distributed File System (HDFS).
  • Jupyter Notebook: Users can create and share documents with live code, equations, visualizations, and narrative text using the open-source web application Jupyter Notebook. It is frequently used for teamwork, data exploration, and analysis.
  • KNIME: The open-source data analytics platform KNIME allows users to visually design data workflows using a drag-and-drop interface. It offers a large selection of integrated tools and preprocessing, analysis, and reporting capabilities.
  • SAS: Data management, business intelligence, and advanced analytics are all handled by the SAS software suite. It provides a large selection of features and tools for data mining, predictive modelling, and statistical analysis.
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Key Features

ACTE Bangalore offers Data Analytics Training in more than 27+ branches with expert trainers. Here are the key features,

  • 40 Hours Course Duration
  • 100% Job Oriented Training
  • Industry Expert Faculties
  • Free Demo Class Available
  • Completed 500+ Batches
  • Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Admedabad.

Curriculum

Syllabus of Data Analytics Training in Hyderabad
Module 1: Foundations of Data Analysis
  • Static Analysis
  • Counting, Probability, and Probability Distributions
  • Sampling Distributions
  • Estimation and Hypothesis Testing
Module 2: Exploratory Data Analysis
  • Scatter Diagram
  • ANOVA and Chi-square
  • Data Cleaning
  • Correlation and Regression
  • Imputation Techniques
Module 3: Data Analytics Overview
  • Importance of Data Analytics
  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Benefits of Data Analytics
Module 4: Data Visualization
  • Graphical Techniques
  • Data Types, Measure Of central tendency, Measures of Dispersion
  • Skewness & Kurtosis
  • Box Plot
  • Data Visualization Tools
Module 5: Basic Excel for Data Analysis
  • The CONCATENATE Function
  • Absolute Cell References
  • Data Validation
  • Conditional Formatting
  • Using the IF Function
  • Pivot Tables
  • Charts
  • Difference between Tables and Ranges
  • Create Table
  • Removing Unwanted Characters from Text
Module 6: Advanced Excel for Data Analysis
  • Date Formats
  • Conditional Formatting
  • Filtering
  • Lookup Functions
  • Sorting
  • Pivoting
Module 7: Structured Query Language (SQL)
  • SQL SELECT Statement
  • Usage of Single-Row Functions
  • Invoke Conversion Functions
  • Conditional Expressions
  • Aggregate Data Using the Group Functions
  • Display Data From Multiple Tables Using Joins
  • Use Sub-queries to Solve Queries
  • The SET Operators
  • Data Manipulation Statements
  • Use of DDL Statements to Create and Manage Tables
  • Management of Schema Objects
  • Manipulate Large Data Sets
  • Data Management in Different Time Zones
  • Retrieve Data Using Sub-queries
  • Regular Expression Support
Module 8: Data Visualization Tools- Power BI
  • Introduction to Power BI
  • Power BI Desktop
  • Data Source Connection
  • Data Transforming
  • Modeling using Power BI
  • Data Visualization
  • Reports and Dashboards
  • Publishing and Sharing Reports
Module 9: Data Visualization Tools- Tableau
  • Tableau Basic Reports
  • Tableau Charts
  • Tableau Advanced Reports
  • Calculations & Filters
  • Tableau Dashboards
  • Server
Module 10: Python for Data Analysis
  • Basics of Python
  • Program Flow / Data Flow
  • Functions
  • File Handling
  • Exception Handling
  • OOP Concepts
  • Comprehension in Python
  • Thread and DateTime
  • Advanced data Structure/collections
  • GUI programming with Python
  • API access with Python
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Get Hands-on Knowledge about Real-Time Data Analytics Projects

Project 1
Healthcare Analytics

Students analyze patient data to identify disease trends, predict patient outcomes, or optimize healthcare resource allocation.

Project 2
Supply Chain Optimization

Students analyze supply chain data to optimize inventory management, reduce costs, and improve delivery efficiency.

Boost Your Career With Our Data Analytics Placement Support Program

  • Internships are included in the curriculum for Data Analytics placement. Students will get practical experience working in organizations on real-world data analytics projects through these internships.
  • We organize career fairs, guest lectures by industry professionals, and networking events with recruiters. At these events, students can network, find employment opportunities, and learn about industry trends.
  • Through industry projects, students can work with partners in the industry to solve practical data analytics problems in this course. This hands-on experience offers both the practical application of skills and exposure to industry-relevant problems.
  • Data analytics placement typically offers career guidance and support services to assist students in developing job search strategies, improving their resumes and cover letters, and preparing for job interviews.
  • Data Analytics Placement provides professional development workshops and seminars on topics such as networking techniques, career advancement tactics, and interview preparation.
  • After completing the course, students can obtain jobs with our placement support. Help with job searches, employer recommendations, and access to job postings and recruitment tools are a few examples.

Succeed Our Resourceful Data Analytics Certification

A Data Analytics Certification is not just a qualification, it's a transformative journey that elevates an individual's proficiency and expertise in data analytics. These certifications delve into a wide range of topics, from big data technologies to machine learning, data visualization to statistical methodologies, and data analysis techniques. The accomplishment of a Data Analytics Certification program is a testament to a candidate's ability to collect, process, examine, and evaluate data, empowering them to make informed decisions and reach actionable conclusions. Obtaining a Data Analytics Certification is not just about boosting employment prospects and increasing earning potential, it's about verifying expertise in a rapidly developing field that is essential to many industries, including technology, healthcare, banking, and business.

Actually, getting certified in data analytics can greatly improve your chances of landing a job. With this certification, you can show off your expertise in data analytics methods to potential employers in a variety of sectors who are looking for people with the ability to gather, analyze, and interpret data to support well-informed decisions.
  • Data Analyst
  • Data Scientist
  • Big Data Engineer
  • Data Engineer
  • Business Intelligence Developer
Data analytics certificates provide numerous job advantages, such as improved employability, increased earning potential, and prospects for career advancement. They attest to one's competence in data analysis methods, bolster one's reputation with potential employers, and provide access to a variety of positions in sectors that require qualified individuals to glean insights from data.
  • Data Science Certification
  • Data Engineering Certification
  • Big Data Analytics Certification
  • Business Intelligence (BI) Certification
The duration of the program and the candidate's level of experience determine how long it takes to become certified in data analytics. The beauty of these certification programs is their flexibility, with durations that can vary from a few weeks to several months. This allows students to tailor their learning experience to their schedule, ensuring they can study and pass exams at their own pace.

Complete Your Course

a downloadable Certificate in PDF format, immediately available to you when you complete your Course

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a physical version of your officially branded and security-marked Certificate.

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Receive Training From Our Skilled and Effective Data Analytics Trainers

  • Throughout their educational journey, they provide students with support and guidance, feedback, questions answered, and assistance in overcoming obstacles so they can succeed in their Data Analytics pursuits.
  • They offer students insightful advice and valuable insights because they frequently have extensive practical experience working on real-world data analytics projects across various industries.
  • Our data analytics trainers have excellent communication and teaching abilities, which help them to successfully and clearly explain difficult ideas and methods to students.
  • Through projects and case studies, our trainers give students the chance to apply their knowledge and skills in real-world scenarios, emphasizing experiential learning and practical exercises.
  • Our data analytics trainers are highly experienced in the real world and bring a wealth of knowledge from their positions at Google, Microsoft, Netflix, Amazon, and Uber to the classroom.
  • Through ongoing training and continuing education, our trainers remain current on the newest developments, technologies, and trends in the field of Data Analytics Training in Hyderabad.

Data Analytics Course FAQs

Are you looking for a better deal at a lower price?

Give us a call at +91 93833 99991 to see what amazing offers are waiting for you!
  • ACTE provides students with placement assistance
  • Continuous support from the placement cell is available until employment is secured
  • ACTE maintains relationships with over 700 MNCs for placement opportunities
  • Access the lifetime Student Portal for job listings, study materials, videos, and interview preparation
  • ACTE's commitment to student success is reflected in its 85%+ placement rate
  • Validate placements by viewing the Placed Student list on the website
ACTE is well-known for having a proven track record of assisting students in obtaining employment at prestigious multinational companies like SAP, Oracle, and Accenture. With over 3500 students placed overseas in the previous year and an amazing 85% placement rate, ACTE promises a wealth of resources and support for career advancement.
Yes, some practical training is included in the data analytics course. Students apply theory concepts, analyze datasets, and develop data-driven solutions with the help of case studies, practical exercises, and real-world projects. As a result, they acquire priceless experience utilizing tools and methods associated with data analytics.
All of ACTE's trainers are working professionals with between 10 and 16 years of relevant technical experience. With their training in ACTE and as subject matter experts, they can offer a great learning opportunity. Our trainers use real-world examples and their own experiences to enhance learning and ensure that students receive excellent instruction that prepares them for the workforce.
You can use ACTE to watch the classes that have been recorded if you are unable to attend in person. Through our Student Portal, you can get study materials and get in touch with the teacher for on-going advice and assistance.
The Data Analytics Course provides a variety of learning options, including self-paced learning with access to study materials and recorded sessions through the Student Portal, online classes, and classroom sessions.

Why Should I Sign Up for a Data Analytics Course at ACTE?

Enrolling in an ACTE Data Analytics course gives you access to professional training, comprehensive placement assistance, study guides, session recordings, and interview tips. Strong career opportunities and support are ensured by ACTE because of its partnerships with over 700 MNCs and over 85% placement rate.
Prospective students are welcome to visit ACTE for an orientation or trial class prior to enrolling. It allows people to engage with instructors, experience the teaching style, and assess the course material before deciding whether or not to enroll.
Yes, you will receive a course completion certificate upon finishing the course, which acts as an official acknowledgement of the data analytics knowledge and skills you have gained.
The top training provider, ACTE, has been offering top-notch instruction in a range of subjects for more than 15 years, including data analytics. As a result, it has solidified its reputation as a reliable source of information.
In order to ensure that every participant receives individualized attention and efficient learning, ACTE maintains a low student-teacher ratio with small class sizes, typically consisting of 10 to 15 students.
There are typically options for morning, afternoon, or evening classes on weekdays and weekends. The frequency of the course, the student, and the instructor's availability may all affect the schedule.
You can contact our support number at +91 93800 99996 / Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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