Data Science Training
4.4 /5- Offered byInternshala
Data Science Training at Internshala Overview
Duration | 6 weeks |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
Data Science Training at Internshala Highlights
- Placement Training
- Certificate of Completion
- Hands On Exercises
Data Science Training at Internshala Course details
- Introduction to Data Science
- Data Science Overview
- Basic Python for Data Science
- 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
- Understanding Statistics of Data Science
- Introduction to Statistics
- Measures of Central Tendency
- Understanding the spread of data
- Data Distribution
- Introduction to Probability
- Probabilities of Discreet and Continuous Variables
- Central Limit Theorem and Normal Distribution
- Introduction to Inferential Statistics
- Understanding the Confidence Interval and margin of error
- Hypothesis Testing
- T tests
- Chi Squared Tests
- Understanding the concept of Correlation
- Predictive Modeling and the basics of Machine Learning
- 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
- With every organisation betting big on Data Science to create more value for business, the demand for Data Scientists has skyrocketed
- Master the building blocks of Data Science - Python, Statistics, and Predictive Modeling
Data Science Training at Internshala Curriculum
Introduction to Data Science
Overview of Data Science
Terminologies in Data Science
Applications of Data Science
Python for Data Science
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
Understanding the Statistics for Data Science
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
Predictive Modeling and Basics of Machine Learning
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
The Final Project
Final Project
Data Science Training at Internshala Entry Requirements
- This program is for beginners. There are no prerequisites.
- Not mentioned
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