Data Analytics Course Online 

Learning Outcomes

• Become proficient in using Excel for data cleaning, manipulation, and analysis.
• Master the use of formulas, functions, and pivot tables to analyze complex datasets.
• Develop a comprehensive understanding of advanced Excel techniques for data analysis.
• Learn how to visualize data and communicate insights effectively using Excel.

Topics Covered

• The Module 2 - Excel and Data Preparation introduces learners to the use of Excel for data analysis. It focuses on data cleaning and preparation techniques in Excel, fundamental for data analysis. The module teaches how to analyze data using various formulas and functions within Excel and explains the utility of pivot tables and data visualization features within the application. The module wraps up with a dive into advanced Excel techniques that offer a more profound exploration of data. Download Curriculum to Know the topics clearly.

Learning Outcomes

• Understand the basics of SQL and relational databases and become proficient in writing SQL queries.
• Learn advanced SQL techniques, including joins, aggregations, and data cleaning/validation.
• Become familiar with concepts like indexing and performance optimization in SQL.
• Gain hands-on experience with SQL to retrieve, manipulate, and analyze data from databases.

Topics Covered

• The Module 3 - SQL and Data Retrieval provides a basic understanding of SQL and relational databases, key tools for a data analyst. Learners get to write SQL queries, and understand how to retrieve and filter data. The module extends to advanced SQL query techniques, including the use of joins and aggregations. It further details data cleaning and validation using SQL and touches upon indexing and performance optimization in SQL databases. Download Curriculum to Know the topics clearly.

Learning Outcomes

• Understand the principles of data visualization and become proficient in using Tableau.
• Learn how to connect, prepare, and analyze data using Tableau.
• Master the creation of interactive dashboards and visualizations in Tableau.
• Understand best practices in dashboard design and gain expertise in advanced data analysis techniques using Tableau.

Topics Covered

• In Module 4 - Data Visualization with Tableau introduces Tableau as a leading data visualization tool. It discusses connecting and preparing data for use in Tableau and walks learners through the creation of interactive visualizations and dashboards. The module dives into advanced techniques for data analysis within the Tableau interface. Finally, the module brings together dashboard design principles and best practices to guide the creation of visually engaging and informative data presentations. Download Curriculum to Know the topics clearly.

Learning Outcomes

• Become familiar with the concept of business intelligence and master the use of Power BI for data cleansing, transformation, and analysis.
• Learn to create interactive visualizations and reports in Power BI.
• Understand the application of advanced analytics and machine learning in Power BI.
• Gain a deep understanding of the role of Power BI in data-driven decision-making and how it can be used for sales and marketing analytics.

Topics Covered

• This module 5 - Data Analysis with Power BI begins with an introduction to Power BI and its role in business intelligence. The module teaches data cleansing and transformation techniques within Power BI and then focuses on data modeling and DAX functions. It leads to interactive visualization and reporting features within the tool. Advanced analytics and machine learning capabilities within Power BI are also discussed. The module highlights the use of Power BI for business analysis, key metrics, and KPIs, and data-driven decision-making. Furthermore, it elaborates on how Power BI can be used for sales and marketing analytics. Download Curriculum to Know the topics clearly.

Learning Outcomes

• Gain a comprehensive understanding of Python as a tool for data analysis.
• Learn to manipulate and analyze data using pandas, a popular Python library.
• Develop skills in exploratory data analysis and data visualization using Python libraries like Matplotlib and Seaborn.
• Master the concepts and techniques of statistical analysis and hypothesis testing using Python.

Topics Covered

• Module 6 -  Data Analysis with Python introduces Python as a versatile language for data analysis. Performing Exploratory Data Analysis (EDA) using Python is a significant part of this module. It also incorporates data visualization using Matplotlib and Seaborn, two essential libraries in Python. Finally, the module wraps up with a detailed look at statistical analysis and hypothesis testing using Python, vital skills for any data analyst. Download Curriculum to Know the topics clearly.

Learning Outcomes

• Apply the skills and knowledge acquired from the course in a practical work setting.
• Gain experience working on real data analytics projects.
• Develop professional skills such as teamwork, communication, and problem-solving.
• Receive feedback on performance and areas of improvement.
• Understand the challenges and dynamics of working in a data analytics role.

Topics Covered

• In the final module, learners will undertake a one-month virtual internship to apply the skills and knowledge they have acquired. They will gain practical experience and receive feedback on their work, which is vital in transitioning from classroom learning to real-world application. This hands-on experience will help them understand and navigate the challenges of working as a data analyst.