Certified Data Science with Python Professional

How It Works

  1. 1. Select Certification & Register
  2. 2. Receive a.) Online e Learning Access (LMS)    b.) Hard copy - study material
  3. 3. Take exam online anywhere, anytime
  4. 4. Get certified & Increase Employability

Test Details

  • Duration: 60 minutes
  • No. of questions: 50
  • Maximum marks: 50, Passing marks: 25 (50%).
  • There is NO negative marking in this module.
  • Online exam.

Benefits of Certification

$49.00 /-
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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.

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

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.

Data Science with Python Blogs

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Data Science with Python Jobs

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Data Science with Python Internships

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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

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