Python with Data Science
Become a Data Science Expert & learn How to Apply Them in Real-world Applications from Training Basket. Enroll Now!
Course Duration - 3 Months
Course Content
Python With Data Science Course

Module 1: Python
- Environment set-up
- Jupyter overview
- Python Numpy
- Python Pandas
- Python
- Matplotlib
Module 2: INTRODUCING DATA SCIENCE
- What is Data science..?
- Explore the Data science workflow
- Python Packages for data science
- Installing Jupyter
Module 3: R
- An introduction to R
- Data structures in R
- Data visualization with R
- Data analysis with R
Module 4: Statistics
- Types of variables
- Measures of central tendency
- Measures of variability
- Coefficient of variance
- Skewness and Kurtosis
Module 5: Inferential statistics
- Normal distribution
- Test hypotheses
- Central limit theorem
- Confidence interval
- T-test
- Type I and II errors
- Student’s T distribution
Module 6: visualization with matplotlib
- Simple Line Plots
- Scatter Plots
- Legends and annotations
- Heatmaps
- Subplots
- Plotting in Pandas
Module 7: Exploratory data analysis
- Data visualization
- Missing value analysis
- The correction matrix
- Outlier detection analysis
Module 8: Supervised machine learning
- Python Scikit tool
- Neural networks
- Support vector machine
- Logistic and linear regression
- Decision tee classifier
Module 9: Tableau
- Working with Tableau
- Deep diving with data and connection
- Creating charts
- Mapping data in Tableau
- Dashboards and stories
Module 9: Machine learning on cloud
- In this lesson, you will learn –
- ML on cloud platform
- ML on AWS
- ML on Microsoft Azure
Module 10: machine learning
- What is Machine Learning..?
- Introducing Scikit-learn
- Types of ML
- Basic steps of ML
- Data preprocessing
- Dealing with missing data
- Handling with categorical data
- Features scaling
- Splitting data
- Linear Regression
- Naive Bayes Classification
- Logistic Regression
- Support Vector Machine
- Evaluate Classificaion model Performance
- Principal Component Analysis
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