Curriculum
6 Sections
18 Lessons
10 Weeks
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Module 1: Introduction to Data Science
3
1.1
What is Data Science?
1.2
Importance of Data Science in different industries
1.3
Overview of Data Science workflow
Module 2: Python for Data Science
3
2.1
Installing Python and setting up Jupyter Notebook
2.2
Python basics: variables, data types, loops, and functions
2.3
Libraries: NumPy, Pandas, Matplotlib, and Seaborn
Module 3: Data Wrangling & Cleaning
3
3.1
Handling missing data and duplicates
3.2
Data manipulation using Pandas
3.3
Working with different file formats (CSV, JSON, Excel)
Module 4: Data Visualization
3
4.1
Creating plots with Matplotlib & Seaborn
4.2
Understanding histograms, scatter plots, bar plots, and heatmaps
4.3
Interactive visualizations with Plotly
Module 5: Introduction to Statistics & Probability
3
5.1
Descriptive statistics (mean, median, mode, variance, standard deviation)
5.2
Probability distributions and their applications
5.3
Hypothesis testing and confidence intervals
Module 6: Introduction to Machine Learning
3
6.1
Overview of supervised vs. unsupervised learning
6.2
Introduction to Scikit-learn
6.3
Implementing a simple linear regression model
Data Science – Beginner
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