Data science has become a great trend in computational and predictive statistical analysis. It’s also used by various organizations to make data-driven decisions. With all the challenges faced, Python has become an indispensable tool for the data science analyst and an important tool for any data scientist.

Vskills Certification Course in Data Science with Python will provide a means of transcending the theory of data science with the help of Python and many other integrated toolsets.

In this course, you will learn the following concepts in depth.

Introduction to Data Science
Python Essentials for Data Science
Data ScienceToolBox
Importing & Cleaning Data
Data Visualization with Statistical Thinking
Introduction to Machine Learning with Scikit-Learn
Practice with Case Studies

Note: You will receive an online access to e-learning (videos), hard copy material is not applicable to this course.

Why should one take Data Science with Python Certification?

This Course is intended for Individuals wanting to understand a deeper level of data science using more advanced techniques and operations and individuals wanting to expand their knowledge of Python while learning data science; IT specialists aspiring to learn a new skill set; statisticians; computer scientists; and IT analysts etc.

Earning Vskills Data Science with Python Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.

Who will benefit from taking Data Science with Python Certification?

IT specialists aspiring to learn a new skill set; statisticians; computer scientists; and IT analysts, software engineer who want to become data scientist etc.

Companies that hire Data Science with Python Professional

Data Science with Python is one of the faster growing filed and are in great demand. Companies like KPMG, Accenture, TCS & Cognizant specializing in Data Science related activities are constantly looking for certified professionals.

Companies that hire Data Science with Python Professionals

There is a huge demand for data science professionals having python, R, SQL programming skills. Companies like TCS, Amazon, Accenture, Flipkart, Wipro, Capgemini, IBM, Cognizant etc are looking for certified professionals.

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Data Science with Python Jobs

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Data Science with Python Internships

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TABLE OF CONTENT

Introduction to Data Science

Data Science Introduction and Use Cases
Data Science Roles and Lifecycle
Data Science Stages and Technologies
Data Science Technologies and Analytics
ML-Data and CRISP-DM

Statistical Techniques

Statistics and Experiments
Types of Data and Descriptive Statistics
Random Variables and Normal Distribution
Histograms and Normal Approximation
Central Limit Theorem
Probability Theory
Binomial Theory - Expected Value and Standard Error
Hypothesis Testing

Python for Data Science

Introduction to Python
Starting with Python with Jupyter Notebook
Python Variables and Conditions
Python Iterations 1
Python Iterations 2
Python Lists
Python Tuples
Python Dictionaries 1
Python Dictionaries 2
Python Sets 1
Python Sets 2
NumPy Arrays 1
NumPy Arrays 2
NumPy Arrays 3
Pandas Series 1
Pandas Series 2
Pandas Series 3
Pandas Series 4
Pandas DataFrame 1
Pandas DataFrame 2
Pandas DataFrame 3
Pandas DataFrame 4
Pandas DataFrame 5
Pandas DataFrame 6
Python User-Defined Functions
Python Lambda Functions
Python Lambda Functions and Date-Time Operations
Python String Operations

Exploratory Data Analysis (EDA)

Introduction to EDA
EDA Tools and Processes
EDA Project - 1
EDA Project - 2
EDA Project - 3
EDA Project - 4
EDA Project - 5
EDA Project - 6
EDA Project - 7

Machine Learning

Introduction to Machine Learning
Machine Learning Terminology
History of Machine Learning
Machine Learning Use Cases and Types
Role of Data in Machine Learning
Challenges in Machine Learning
Machine Learning Lifecycle and Pipelines
Regression Problems
Regression Models and Performance Metrics
Classification Problems and Performance Metrics
Optimizing Classification Metrics
Bias and Variance

Linear Regression

Linear Regression Introduction
Linear Regression - Training and Cost Function
Linear Regression - Cost Functions and Gradient Descent
Linear Regression - Practical Approach
Linear Regression - Feature Scaling and Cost Functions
Linear Regression OLS Assumptions and Testing
Linear Regression Car Price Prediction
Linear Regression Data Preparation and Analysis 1
Linear Regression Data Preparation and Analysis 2
Linear Regression Data Preparation and Analysis 3
Linear Regression Model Building
Linear Regression Model Evaluation and Optimization
Linear Regression Model Optimization

Logistic Regression

Logistic Regression Introduction
Logistic Regression - Logit Model
Logistic Regression - Telecom Churn Case Study
Logistic Regression - Data Analysis and Feature Engineering
Logistic Regression - Build the Logistic Model
Logistic Regression - Model Evaluation - AUC-ROC
Logistic Regression - Model Optimization
Logistic Regression - Model Optimization 2

Unsupervised Learning - K-Means Clustering

Unsupervised Learning - K-Means Clustering
K-Means Clustering Computation
K-Means Clustering Optimization
K-Means - Data Preparation and Modelling
K-Means - Model Optimization

Naive Bayes Probability Model

Naive Bayes Probability Model - Introduction
Naive Bayes Probability Computation
Naive Bayes - Employee Attrition Case Study
Naive Bayes - Model Building and Optimization

Classification using decision trees

Decision Tree - Model Concept
Decision Tree - Learning Steps
Decision Tree - Gini Index and Entropy Measures
Decision Tree - Hyperparameter Tuning
Decision Tree - Iris Dataset Case Study
Decision Tree - Model Optimization using Grid Search Cross Validation

Ensemble Methods – Random Forest

Random Forest - Ensemble Techniques Bagging and Random Forest
Random Forest Steps Pruning and Optimization
Random Forest - Model Building and Hyperparameter Tuning using Grid Search CV
Random Forest - Optimization Continued

Advanced Classification Techniques – Support Vector Machine

Support Vector Machine Concepts
Support Vector Machine Metrics and Polynomial SVM
Support Vector Machine Project 1
Support Vector Machine Predictions
Support Vector Machine - Classifying Polynomial Data

Dimensionality Reduction Using PCA

Principal Component Analysis - Concepts
Principal Component Analysis - Computations 1
Principal Component Analysis - Computations 2
Principal Component Analysis Practical

Dimensionality Reduction Using PCA

Principal Component Analysis - Concepts

Certified Data Science with Python Professional

How It Works

Test Details

Benefits of Certification

Why should one take Data Science with Python Certification?

Who will benefit from taking Data Science with Python Certification?

Companies that hire Data Science with Python Professional

Data Science with Python Table of Contents

Data Science with Python Sample Questions

Data Science with Python Interview Questions

Data Science with Python Practice Test

Data Science with Python Tutorials

Companies that hire Data Science with Python Professionals

Apply for Data Science with Python Certification

TABLE OF CONTENT

Introduction to Data Science

Statistical Techniques

Python for Data Science

Exploratory Data Analysis (EDA)

Machine Learning

Linear Regression

Logistic Regression

Unsupervised Learning - K-Means Clustering

Naive Bayes Probability Model

Classification using decision trees

Ensemble Methods – Random Forest

Advanced Classification Techniques – Support Vector Machine

Dimensionality Reduction Using PCA

Dimensionality Reduction Using PCA