Data Analytics with Power BI and Tableau

Get job-ready with India’s premier “Data Analytics with Power BI & Tableau” featuring an industry-focused curriculum with hands-on live projects & internships. Get ahead & land your first job with easy access to exclusive job opportunities with our extensive network of over 2000+ hiring partners.

About Program

Curriculum

  • Introduction to Data Analytics: Overview of data analytics, its importance, and applications in various industries.
  • Introduction to SQL: Basics of SQL, its role in data analytics, and its relevance in working with relational databases.
  • Working with MySQL/Oracle: Installation and setup of MySQL/Oracle, creating databases, and tables, and performing basic SQL queries.
  • Data Manipulation with SQL: SELECT, INSERT, UPDATE, DELETE statements, filtering data, sorting, joining tables, and aggregating data.
  • Advanced SQL Queries: Subqueries, views, stored procedures, and user-defined functions in SQL.
  • Introduction to Python: Overview of Python programming language, its features, and advantages in data analytics.
  • Python Libraries for Data Analytics: Introduction to popular Python libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization.
  • Data Cleaning and Preprocessing: Handling missing values, data transformation, feature engineering, and data normalization using Python.
  • Exploratory Data Analysis (EDA): Techniques to explore and visualize data using Python libraries, identifying patterns, outliers, and relationships.
  • Data Wrangling and Transformation: Techniques for transforming and reshaping data, merging datasets, and dealing with large datasets.
  • Introduction to NoSQL Databases: Understanding the differences between SQL and NoSQL databases, advantages of NoSQL for data analytics.
  • Introduction to MongoDB: Installation and setup of MongoDB, creating databases, collections, and performing basic CRUD operations.
  • Querying MongoDB: Performing data retrieval, filtering, sorting, aggregation, and indexing in MongoDB.
  • Data Modeling in MongoDB: Designing efficient data models for NoSQL databases, embedding and referencing documents, and schema design considerations.
  • Integration of MongoDB with Python: Connecting MongoDB with Python, querying MongoDB using Python, and performing data analytics tasks.
  • Introduction to Statistics: Basic concepts in statistics, descriptive statistics, measures of central tendency, and variability.
  • Probability and Distributions: Probability theory, common probability distributions (normal, binomial, etc.), and their applications in data analytics.
  • Hypothesis Testing: Understanding hypothesis testing, types of tests (t-tests, chi-square tests, etc.), and their applications in data analysis.
  • Regression Analysis: Linear regression, multiple regression, logistic regression, and their interpretation in data analytics.
  • Data Sampling and Confidence Intervals: Techniques for sampling data, constructing confidence intervals, and understanding their significance.
  • Introduction to Data Visualization: Importance of data visualization, principles of effective visual design, and storytelling with data.
  • Introduction to Tableau: Installation and setup of Tableau, connecting to data sources, creating visualizations, and applying filters and parameters.
  • Advanced Visualization Techniques: Creating dashboards, interactive visualizations, and advanced chart types in Tableau.
  • Introduction to Power BI: Installation and set up of Power BI, connecting to data sources, creating reports, and applying filters and slicers.
  • Advanced Features in Power BI: Creating interactive dashboards, using DAX expressions, and sharing and publishing reports.

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Frequently Asked Questions

Absolutely! Our course is designed to cater to learners of all levels, including beginners. We start with the basics and gradually progress to advanced Data Science with Power BI concepts.
This course covers various tools and technologies, including Tableu, Power BI Desktop, Power BI Service, DAX, M Query Language, Power Query Editor, Power BI Mobile App, Power BI Report Server, Power BI Embedded, Power Automate, and Power Apps.
Yes, upon successful completion of the course, you will receive a certificate that verifies your participation and successful acquisition of skills in Data Analytics with Power BI and Tableu.
The course is designed to be flexible, allowing you to learn at your own pace. However, we recommend dedicating a minimum of 6-8 hours per week to effectively grasp the concepts and complete the practical exercises.
Yes, you will have lifetime access to the course materials, including pre-recorded video lectures, exercises, and supplementary resources. You can refer back to them even after completing the course.
Absolutely! Our course includes a discussion forum where you can interact with instructors. You can ask questions, seek clarification, and engage in meaningful discussions throughout your learning journey.
24/7 Chatbot support is available & Our dedicated support team is available to assist you with any technical issues or course-related queries.
Yes, the course includes hands-on projects and assignments that allow you to apply the concepts you learn. These projects will help you build a portfolio and showcase your skills as a Data Scientist or Data Analyst.
Absolutely! The course provides you with a strong foundation and practical skills required for Data Science, enhancing your employability. We also offer career guidance and 100% job placement assistance to our students.

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