Learn Data Science
4.4 /5- Offered byInternshala
Learn Data Science at Internshala Overview
Duration | 6 weeks |
Total fee | ₹1,799 |
Mode of learning | Online |
Difficulty level | Beginner |
Credential | Certificate |
Learn Data Science at Internshala Highlights
- Certificate of Training from Internshala Trainings
- Placement assistance to build your career
- Downloadable content with lifetime access
- Free Placement Preparation Training
Learn Data Science at Internshala Course details
- Introduction to Data Science
- Python for Data Science
- Understanding the Statistics for Data Science
- Predictive Modeling and Basics of Machine Learning
- The Final Project
- Data science is a heady mix of maths, statistics, and programming
- Videos available to learn various concepts
- Get hands on practice by doing projects
- Build 2 projects for hands-on practice
Learn Data Science at Internshala Curriculum
MODULE: 1
Overview of Data Science
Terminologies in Data Science
Applications of Data Science
MODULE: 2
Introduction to Python
Understanding Operators
Variables and Data Types
Conditional Statements
Looping Constructs
Functions
Data Structure
Lists
Dictionaries
Understanding Standard Libraries in Python
Reading a CSV File in Python
Data Frames and basic operations with Data Frames
Indexing Data Frame
MODULE: 3
Introduction to Statistics
Measures of Central Tendency I
Measures of Central Tendency II
Understanding the spread of data
Data Distribution
Introduction to Probability
Probabilities of Discreet and Continuous Variables
Central Limit Theorem and Normal Distribution I
Central Limit Theorem and Normal Distribution II
Introduction to Inferential Statistics
Understanding the Confidence Interval and margin of error
Hypothesis Testing
T tests I
T tests II
Chi Squared Tests
Understanding the concept of Correlation
MODULE: 4
Introduction to Predictive Modeling
Understanding the types of Predictive Models
Stages of Predictive Models
Hypothesis Generation
Data Extraction
Data Exploration
Reading the data into Python
Variable Identification
Univariate Analysis for Continuous Variables
Univariate Analysis for Categorical Variables
Bivariate Analysis
Treating Missing Values
How to treat Outliers
Transforming the Variables
Basics of Model Building
Linear Regression
Logistic Regression
Decision Trees
K-means
Data Set for Final Test
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