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Data Analytics - Skill Up

Self-Paced Course
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interested count34k+ interested Geeks

The Data Analytics Course is a practical, beginner-to-advanced course designed to build a strong foundation in analytics and business intelligence. Covering Python, statistics, EDA, visualization, SQL, Excel, Power BI, and Tableau, the course equips learners to handle real-world data and drive insights through analysis. Ideal for aspiring data analysts, business analysts, or anyone interested in data-driven decision making.

course duration9 Weeks
interested count34k+ interested Geeks

Course Overview

This 9-week course offers a structured, hands-on curriculum to help you learn the complete data analytics pipeline from scratch. Through theory, coding practice, real-world tools, and guided projects, learners build analytical thinking, technical skills, and the ability to solve data problems with confidence.

Course Highlights

  • Learn Python programming for data analytics
  • Build strong foundations in statistics, probability and hypothesis testing
  • Perform EDA using Pandas, NumPy and Matplotlib
  • Handle missing values, outliers and duplicates
  • Master data visualization with Seaborn, Plotly, Power BI and Tableau
  • Perform Web Scraping using BeautifulSoup and Selenium
  • Conduct SQL-based data querying and aggregation
  • Explore Excel analytics: Pivot Tables, Functions, Dashboards
  • Build dynamic dashboards and reports in Power BI & Tableau
  • Complete multiple real-world projects for portfolio development
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Course Content

01Week 0: Basic Python
  • What is Data Analytics and its Importance
  • Python Installation & Setup
  • Input/Output, Variables, Keywords
  • Data Types, Operators, Conditional Statements
  • Loops and Functions
  • Strings, Lists, Dictionaries, Tuples, Sets
  • Python Collections, Comprehensions
  • Error Handling, File Handling, Generators, Decorators
02Week 2: Maths for Data Analytics
  • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
  • Range, Quartiles, Percentiles
  • Probability: Basic Concepts & Distributions (Normal, Binomial, Poisson, etc.)
  • Covariance and Correlation
  • Hypothesis Testing: CLT, Z-test, T-test, ANOVA, MANOVA
  • Non-parametric Tests: Mann-Whitney, Kruskal-Wallis
  • Data Skewness Detection and Handling
03Week 3: NumPy and Pandas
  • NumPy Basics, Arrays, Indexing, Broadcasting
  • Getting Started with Pandas: Series & DataFrames
  • CRUD Operations
  • Data Exploration: info(), describe(), value_counts(), head(), tail()
  • Grouping, Aggregation, Sorting, Filtering, reset_index()
  • Handling Missing Data
  • Outlier Detection (IQR, Z-score)
  • Removing Duplicates
04Week 4: Data Visualization
  • Visualization with Matplotlib and Seaborn
  • Interactive Charts using Plotly
  • Correlation Matrix & Heatmaps
  • Time Series Visualization
  • Project: Word Cloud Generation
  • Project: Zomato Data Analysis
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Frequently Asked Questions

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What is Data Analytics?

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Who should take Data Analytics course?

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Do I need prior experience in coding or math?

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What roles can I apply for after Data Analytics course?

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