Python for Data Science & Analytics

What is Python for Data Science & Analytics

Python for Data Science & Data Analytics focuses on leveraging Python’s vast array of libraries and tools specifically tailored for data manipulation, visualization, and machine learning. Participants learn how to utilize libraries like Pandas, NumPy, Matplotlib, and scikit-learn to efficiently handle and analyze data, derive meaningful insights, and build predictive models.

Purpose of Python for Data Science & Analytics

The primary purpose of this course is to empower individuals with the ability to effectively work with data, extract valuable information, and make data-driven decisions. By mastering Python for Data Science & Data Analytics, participants gain a competitive edge in the job market, as data analysis and interpretation skills are in high demand across various industries. Additionally, the course serves as a foundation for those interested in pursuing careers in data science, machine learning, and artificial intelligence.

How it is useful to people if they take the course

Individuals who complete this course are equipped to tackle real-world data challenges, whether it’s analyzing customer behavior, optimizing business processes, or predicting market trends. Moreover, as the demand for data professionals continues to rise, those with expertise in Python for Data Science & Data Analytics are well-positioned to pursue lucrative career opportunities and contribute meaningfully to organizations seeking to derive insights and drive innovation through data.

Module 1 - Overview of Course

1.1 Introduction to Data Science
1.2 Different Sectors Using Data Science
1.3 Value of learning Data Science
1.4 Introduction to Data Analytics

Module 2 - Introduction to Python

2.1 Python Introduction
2.2 Setting up Python Environment
2.3 Python Basics
2.4 Data Types & Operators
2.5 Control & Looping Statements, Functions
2.6 Functions & Lambdas
2.7 Data analysis libraries (NumPy, Pandas, Matplotlib, Seaborn)

Module 3 - Data Preprocessing & Cleaning

3.1 Transforming, Cleaning Data
3.2 Creating Data Models
3.3 Handling Missing Data

Module 4 - EXCEL

4.1 Introduction to Advanced Excel
4.2 Basic Excel Functions & Formulas

Module 5 - Exploratory Data Analysis (EDA)

5.1 Descriptive Statistics
5.2 Working with Charts & Graphs
5.3 Correlation Analysis & heatmaps
5.4 Univariate & Bivariate Analysis

Module 6 - Data Visualization

6.1 Data Visualization using Matplotlib and Seaborn
6.2 Advanced data visualization techniques
6.3 Interactive Visualization
6.4 Geospatial Visualization

Module 7 - Machine Learning Overview

7.1 Machine Learning Introduction
7.2 Supervised Learning Models & Un-Supervised Learning Models
7.3 Linear & Logistic Regression
7.4 Decision Tree & Random Forest

Course Price - 5000/-

With us you can upskill, work on real project, works as interns, get educational consultation, attend mock interviews and redefine your career