Why this course?

Description

Description: (10-12 weeks)

This comprehensive course provides an in-depth understanding of data analytics using multiple tools and libraries, including Python, Tableau, Power BI, and Excel. Starting with Python programming basics and moving through advanced data analysis techniques, this course equips you with the skills needed to analyze, visualize, and present data effectively. You will also work on real-life projects, including building dashboards and performing statistical analysis to provide actionable insights for decision-making.

Overview:

Module 1: Python Data Analytics

Introduction to Python basics, data structures, and programming fundamentals.

Deep dive into Python libraries essential for data analysis, such as Pandas, Numpy, Matplotlib, and Seaborn.

Hands-on projects and assignments focused on manipulating and visualizing data.

Module 2: Data Visualization Tools – Tableau

Introduction to Tableau, a popular data visualization tool for creating interactive and shareable dashboards.

Understanding key Tableau concepts such as Bins, Calculated Fields, Joins, and how to create meaningful visualizations.

Module 3: Data Visualization with Power BI

Learn how to use Power BI, a powerful Microsoft tool for data analysis and visualization.

Modules cover data sources, relationships, new measures, conditional formatting, and popular visualizations.

Module 4: Advanced Excel for Data Analytics

Excel pivot tables, formatting, sorting, filtering, and summarizing data for analysis.

Build Pivot Charts and conduct case studies to analyze salary data using Excel.

Module 5: Statistics and Probability

Core concepts in statistics and probability used to analyze data and derive insights.

Covers topics like distributions, hypothesis testing, and probability theory applied to real-world data analysis.

Projects:

Python Data Analytics Project:

Create a data analysis pipeline using Python libraries such as Pandas and Numpy. Visualize the data using Matplotlib and Seaborn, and analyze insights using statistical techniques.

Tableau COVID-19 Dashboard Project:

Build a fully interactive Tableau dashboard to analyze global COVID-19 data. Use real-time data to create visuals like charts, maps, and time-series graphs.

Power BI HR Data Analytics Project:

Design a comprehensive HR analytics dashboard using Power BI. Visualize employee data, trends, and insights related to recruitment, performance, and turnover.

Excel Salary Data Case Study:

Use Excel to analyze a dataset of employee salaries. Perform data cleaning, summarization, and pivot chart visualization to derive insights from the salary data.

Tools Required:

Python:

Libraries: Pandas, Numpy, Matplotlib, Seaborn

IDE: Jupyter Notebooks or any Python editor like PyCharm or VS Code

Data Analysis: Python libraries for statistical analysis and visualizations

Tableau:

Tableau Desktop for creating interactive dashboards and visualizations.

Tableau Public (optional) for sharing visualizations online.

Power BI:

Power BI Desktop for data analysis, creating relationships, and building dashboards.

Power BI Service (optional) for publishing and sharing reports online.

Microsoft Excel:

Microsoft Excel for performing advanced data analysis with pivot tables, charts, and formulas.

Excel 365 or Office for data manipulation and visualization.

Data Files:

Various datasets (CSV, Excel) related to real-world scenarios like salary, COVID-19, HR data, etc., to work on during assignments and projects.

Skills Required:

Basic Programming Knowledge (Python):

Familiarity with programming concepts such as variables, loops, and functions.

An understanding of data structures like lists, dictionaries, and tuples in Python.

Data Analysis Skills:

Strong understanding of data manipulation and cleaning using tools like Pandas (Python) and Pivot Tables (Excel).

Ability to visualize data using charts, graphs, and dashboards in Tableau, Power BI, and Matplotlib.

Statistical Knowledge:

Basic knowledge of statistics including probability, distributions, hypothesis testing, and correlation to analyze data effectively.

Problem-Solving and Critical Thinking:

Ability to analyze large datasets, extract meaningful insights, and apply data-driven decision-making processes.

Excel Proficiency:

Comfortable working with Excel functions, formulas, and pivot tables for data analysis.