Description

Data Science : Mastering Data Analysis, Machine Learning & Visualization

Our Data Science Course is designed to take you from the basics to advanced concepts, making it perfect for beginners and professionals alike. You’ll start by learning Python programming, covering data structures, loops, and file handling. As you progress, you’ll dive into data manipulation using NumPy and Pandas, followed by data visualization with Matplotlib and Seaborn. The course also introduces key statistical concepts like mean, median, and data distribution, helping you build a strong analytical foundation.

Also, you’ll explore machine learning, covering both supervised and unsupervised techniques like regression, decision trees, and neural networks. You’ll also learn data preprocessing techniques, SQL for database management, Power BI for interactive dashboards, and Advanced Excel for data analysis.

 

Skills you will get

 

Python (Internals, do’s and don’ts) Architecture, Data Structure

• Installation of Anaconda Prompt
• Jupyter Notebook-An Overview
• Shorcut Lkeys in Jupyter Notebook
• Data Types in Python

 

Python, Reading & Writing files in Python

• Rules for Naming the Variables
• List, Tuple, Set, Dictionary
• Introduction to Files and directories Introduction to the command prompt or terminal paths
• Text files Reading from a text file Opening a file using with

 

Loops and conditionals in python

• If, elif and else condition.
• For and While Loop

 

Data Analysis , Manipulation with numpy

• Machine Learning Libraries
• Numpy-Hands on

Pandas Python data science package to manipulate, calculate and analyze data

• Pandas-Hands on

 

Exploratory Data Visualization in Python with matplotlib

• Learn how to explore, visualize, and extract insights from data
• Data Visualization
• Matplotlib-Hands on
• Seaborn hands on

Statistical Thinking in Python (Part 1) Build the foundation

• You need to think statistically and to speak the language of your data
• Measures of Central Tendency
• Measures of Dispersion
• IQR Statistics-Hands-On

 

Supervised Learning & Un-Supervised Learning

• Classification, Regression, Fine-tuning your model
• Supervised Learning
• Unsupervised Learning
• Linear Regression
• Metrics in Linear Regression Hands-on in Linear Regression”

Logistic Regression

• Logistic Regression
• Metrics in Logistic Regression
• Hands-on in Logistic Regression

 

Linear Regression

• Linear regression
• Metrics for Linear regression

Pre-processing for Machine Learning in Python

• Introduction to Data Preprocessing
• Standardizing Data
• Exploratory Data Analysis
• Missing Values
• Outliers
• Standardization , Mnormalization, Feature Scaling and Selection

Tree Based Models Classification and Regression Trees

• Decision Tree
• Bagging
• Boosting Random Forest

Fundamentals of Neural Network

• Neural Network

Project

• Use data science packages, analysis, visualization, create model, extract pure data etc

Advanced Excel

• Excel Functions
• Conditional Formatting
• Pivot tables
• Dashboards

SQL

• Basics of database schema
• Importance SQL Clauses
• SQL Joins

 

Power BI

• Introduction to Power BI desktop
• ETL pipeline in Power BI
• Calculating fields with DAX
• Visualising data with reports
• AI functionalities of Power BI

 

Resume Topics ; Group Discussion Topics ; Interview Topics

• Introduction of Resume and it’s type
• Components of an Effective Resume
• Writing Career Objective
• Preliminary Preparation for Recruitment Process
• LinkedIn Profile – Importance & Creation
• Understanding Group Discussion
• Purpose of a Group Discussion
• Group Discussion Vs. Debate
• Process of Group Discussion
• Essentials of Group Discussion
• Types of interview
• Interview skills
• Effective way of handling interview questions
• Grooming