Python Data Science Training

About Course

Python with Data Science is a dynamic field combining the power of Python programming with the insights of data science. Python’s simplicity and versatility make it the go-to language for data analysis, visualization, and machine learning. Data scientists leverage Python libraries like Pandas, NumPy, and Matplotlib to manipulate and visualize data, while Scikit-Learn and TensorFlow facilitate machine learning tasks. With Python, data scientists can extract valuable insights from vast datasets, build predictive models, and automate complex data-driven processes. This fusion of Python and data science empowers professionals to solve real-world problems, make data-driven decisions, and drive innovation across various industries.

Course Content

Module 01 – Introduction to Data Science using Python

1.1 What is Data Science, what does a data scientist do
1.2 Various examples of Data Science in the industries
1.3 How Python is deployed for Data Science applications
1.4 Various steps in Data Science process like data wrangling, data exploration and selecting the model.
1.5 Introduction to Python programming language
1.6 Important Python features, how is Python different from other programming languages
1.7 Python installation, Anaconda Python distribution for Windows, Linux and Mac
1.8 How to run a sample Python script, Python IDE working mechanism
1.9 Running some Python basic commands
1.10 Python variables, data types and keywords.

Module 02 – Python basic constructs

Module 03 – NumPy for mathematical computing

Module 04 – Pandas for Data manipulation

Module 05 – Data visualization with Matplotlib

Module 06 – Maths for DS-Statistics & Probability

Module 07 – Machine Learning using Python

Module 08 – Supervised learning-Linear Regression

Module 09 – Supervised learning-Logistics Regression

Module 10 – Unsuperwised Leaning And K Means Clusturing

Module 11 – Hire Clustering And Dimention Reduction

Module 12 – Time Series Forecasting

Module 13 – OOPs in Python (Self-paced)

Module 14 – Python integration with Spark (Self-paced)

About Course

What Will You Learn?

Course Content

Module 01 – Introduction to Data Science using Python

1.1 What is Data Science, what does a data scientist do

1.2 Various examples of Data Science in the industries

1.3 How Python is deployed for Data Science applications

1.4 Various steps in Data Science process like data wrangling, data exploration and selecting the model.

1.5 Introduction to Python programming language

1.6 Important Python features, how is Python different from other programming languages

1.7 Python installation, Anaconda Python distribution for Windows, Linux and Mac

1.8 How to run a sample Python script, Python IDE working mechanism

1.9 Running some Python basic commands

1.10 Python variables, data types and keywords.

Module 02 – Python basic constructs

2.1 Introduction to a basic construct in Python

2.2 Understanding indentation like tabs and spaces

2.3 Python built-in data types

2.4 Basic operators in Python

2.5 Loop and control statements like break, if, for, continue, else, range() and more.

Module 03 – NumPy for mathematical computing

3.1 Introduction to mathematical computing in Python

3.2 What are arrays and matrices, array indexing, array math, Inspecting a NumPy array, NumPy array manipulation

Module 04 – Pandas for Data manipulation

4.1 What is a data Manipulation. Using Pandas library

4.2 NumPy dependency of Pandas library

4.3 Series object in pandas

4.4 DataFrame in Pandas

4.5 Loading and handling data with Pandas

4.6 How to merge data objects

4.7 Concatenation and various types of joins on data objects, exploring dataset

Module 05 – Data visualization with Matplotlib

5.1 Introduction to Matplotlib

5.2 Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more

5.3 Matplotlib API

Module 06 – Maths for DS-Statistics & Probability

6.1 Central Tendency

6.2 Variability

6.3 Hypothesis Testing

6.4 Anova

6.5 Correlation

6.6 Regression

6.7 Probability Definitions and Notation

6.8 Joint Probabilities

6.9 The Sum Rule, Conditional Probability, and the Product Rule

6.10 Bayes Theorem

Module 07 – Machine Learning using Python

7.1 Revision of topics in Python (Pandas, Matplotlib, NumPy, scikit-Learn)

7.2 Introduction to machine learning

7.3 Need of Machine learning

7.4 Types of machine learning and workflow of Machine Learning

7.5 Uses Cases in Machine Learning, its various algorithms

7.6 What is supervised learning

7.7 What is Unsupervised Learning

Module 08 – Supervised learning-Linear Regression

8.1 What is linear regression

8.2 Step by step calculation of Linear Regression

8.3 Linear regression in Python

Module 09 – Supervised learning-Logistics Regression

9.1 Logistic Regression

9.2 What is classification

9.3 Decision Tree, Confusion Matrix, Random Forest, Naïve Bayes classifier (Self paced), Support Vector Machine(self paced), XGBoost (self paced)

Module 10 – Unsuperwised Leaning And K Means Clusturing

10.1 Introduction to unsupervised learning

10.2 Use cases of unsupervised learning

10.3 What is clustering

10.4 Types of clustering(self-paced)-Exclusive clustering, Overlapping Clustering, Hierarchical Clustering(self-paced)

10.5 What is K-means clustering

10.6 Step by step calculation of k-means algorithm

10.7 Association Rule Mining(self-paced), Market Basket Analysis(self-paced), Measures in association rule mining(self-paced)-support, confidence, lift

10.8 Apriori Algorithm

Module 11 – Hire Clustering And Dimention Reduction

11.1 Introduction to Dimensionality

11.2 Why Dimensionality Reduction

11.3 PCA

11.4 Factor Analysis

11.5 LDA

Module 12 – Time Series Forecasting

12.1 White Noise

12.2 AR model