Learn Data Analytics with Python: Comprehensive Course

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About This Course

This comprehensive 120-hours program equips you with the skills and knowledge to become a successful data analyst in today's high-demand field. Through a blend of theoretical learning, practical exercises, and project-based work, you'll gain mastery in data analysis from fundamentals to advanced applications.

What You’ll Learn?

Programming for Data Analysis
Foundational Data Analysis Skills
Data Warehousing & Business Intelligence

Advanced Data Analysis Techniques
Project-Based Learning & Portfolio Building

Who is this program for

Seeking to launch a career in data analysis: This program is idealfor those with little to no prior experience in data analysis, but with a strong desire to enter this high-demand field.
Looking to upskill in data analysis: Professionals from various backgrounds, including business analysts, marketing specialists, orrecent graduates with a quantitative background, can benefit from this program to enhance their data analysis skills and transition into a data analyst role.
Interested in mastering data analysis tools and techniques: Whether you have some foundational knowledge or are a complete beginner, this program equips you with the skills to handle data manipulation, visualization, programming, and advanced analysis techniques.
Driven to learn by doing: The program emphasizes project-based learning, allowing you to apply your knowledge to real-world scenarios and build a strong portfolio that showcases your capabilities to potential employers.

Topics for This Course

Introduction to Python Language
Grammar of Python
Python collections: List, Tuple, Set, Dict, Range
Function and Recursion in Python
Object Oriented Programming (OOP) Concept in Python
Exception handling in Python
Regular Expression and Multithreading
Data Structures in Python
Algorithm Design Techniques
Practicing Python libraries

Data Definition Language (DDL): Commands like CREATE, ALTER, and DROP to define and modify the structure of database objects.
Data Manipulation Language (DML): Commands like INSERT, UPDATE, and DELETE to retrieve and manipulate data.
Data Query Language (DML): Commands like SELECT, ORDER BY,GROUP BY, and JOIN to query the database.
Data Control Language (DCL): Commands like GRANT and REVOKE to control access to data.
Transaction Control Language (TCL): Commands like COMMIT and ROLLBACK to manage database transactions.
ACID Properties and Database Normalization.
Data Analysis in & using SQL

HTML: Introduction to HTML, Practicing HTML tags
CSS: CSS Basics, CSS properties for tags, CSS Grid and Flexbox, Responsive Design, Frameworks (e.g., Bootstrap)
JavaScript: Introduction to JavaScript, Datatypes, Variables, Operators, Conditions, Loop, Arrays, Functions, Objects, Object properties and methods, JS Events, JS Strings and methods, JS RegExp, JS Classes, JS Web APIs, JS JSON, jQuery

Introduction to Excel
Functions, Formulas, and Charts
Pivots and Lookups
Understanding Excel Data Tab
Descriptive Statistics: Summary Statistics (Mean, median, mode,range, variance, standard deviation, Quartiles, percentiles), Frequency Distributions (Frequency tables, histograms, bar charts), Data Visualization (Line charts, scatter plots, pie charts, Creating effective visualizations)
Regression Analysis: Simple Linear Regression (Regression equation, correlation coefficient, Residual analysis), Multiple Linear Regression (Multiple independent variables, Model building and evaluation), Non-Linear Regression (Polynomialregression, logarithmic regression)
Time Series Analysis: Time Series Components (Trend, seasonality, cycle, noise), Smoothing Techniques (Moving average, exponential smoothing), Forecasting Methods (Naive forecast, simple exponential smoothing, ARIMA models)

Data Warehousing Concepts: Introduction to data warehouses, data marts, dimensional modeling, and data extraction, transformation, and loading (ETL) processes.
Business Intelligence (BI) Tools & Techniques: Exploring BI tools (e.g., Power BI)for data exploration,reporting, and dashboard creation.
Data Analysis for Business Decisions: Applying data analysis techniques to solve real-world business problems and generate actionable insights.

Machine Learning Fundamentals: Introduction to supervised and unsupervised machine learning concepts, algorithms (regression, classification, clustering), and applications in data analysis.
Data Mining & Text Analysis: Learning techniques for extracting valuable insights from large datasets and unstructured text data.
Big Data Analytics Concepts: Introduction to big data technologies (Hadoop ecosystem, Spark) and tools for handling massive datasets.

Capstone Project: Work on a real-world project, applying the acquired skills to solve a specific business challenge. This will serve as a showcase for your portfolio when seeking job opportunities.
Resume & Interview Preparation: Develop a strong data analyst resume and prepare for technical and behavioral interview questions.

Price: ₹ 59999
Duration: 120 Hours
Modules: 7
Language: English, Hindi
Certificate: Yes