Curriculum

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Course Details

 

S No.Lesson NameTopic Name
1
Data Science Overview
Introduction to Data Science
Different Sectors Using Data Science
Purpose and Components of Python
Key Takeaways
2
Statistical Analysis
Introduction to Statistics
Statistical and Non-statistical Analysis
Major Categories of Statistics
Statistical Analysis Considerations
Population and Sample
Statistical Analysis Process
Data Distribution
Hypothesis testing
Knowledge Check
3
Numpy
Introduction to Numpy
Activity- Sequence it Right
Demo 01-Creating and Printing an ndarray
Knowledge Check
Class and Attributes of ndarray
Basic Operations
Activity-Slice It
Copy and Views
Mathematical Functions of Numpy
4
Pandas
Introduction to Pandas
Knowledge Check
Understanding DataFrame
View and Select Data Demo
Missing Values
Data Operations
Knowledge Check
5
Machine Learning Algorithms (Scikit–Learn)
Machine Learning Approach
Supervised Learning Model
· Regression
§ Linear regression
§ Multiple Linear Regression
· Classification
Logistic regression
SVM
Naive Bayes
Decision Tree
Random Forest
K Nearest Neighbors
Unsupervised Learning Models
§ K-means Clustering
· Apriori association rules
· Sklearn for machine learning model
6
Data Visualization with matplotlib
Introduction to Data Visualization
Line Properties
Scatter plot
Bar graphs and histograms
Pie charts
Subplots
Knowledge Check (MCQ’S)
 Projects 1FedEx regional delivery services delivery time management.
 Project 2Twitter sentiment analysis
 Project 3Product recommendations in ecommerce websites like Amazon
 Interview questions50
 Sample resume3

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