Master in Data Science

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Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.

However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.


This Data Scientist course enables you master skills, such as data analytics, statistical computing, Machine Learning algorithms,and more. It includes multiple hands-on exercises and project work in the domains of banking, finance, entertainment, etc. Devops School's Data Science certification is well recognized across the world and helps you land in your dream job. This Data Scientist course provides detailed learning through live instructor-led sessions that help you gain skills in the shortest possible time. Data Scientists are among the highest-paid and best most in demand professionals. This in-depth Data Scientist course covers ‘What is Data Science?,’ statistical methods, data acquisition and analysis, Machine Learning algorithms, predictive analytics, data modeling, etc.

And how can you do that?

Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)

Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture

The Solution

Data science is a multidisciplinary field. It encompasses a wide range of topics.

  • Understanding of the data science field and the type of analysis carried out
  • Mathematics
  • Statistics
  • Python
  • Applying advanced statistical techniques in Python
  • Data Visualization
  • Machine Learning
  • Deep Learning

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.

So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2020.

e believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).

The Skills

1. Intro to Data and Data Science

Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?

Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.

2. Mathematics

Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.

We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on

Why learn it?

Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.

3. Statistics

You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.

Why learn it?

This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.

4. Python

Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.

Why learn it?

When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.

5. Tableau

Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.

Why learn it?

A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.

6. Advanced Statistics

Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.

Why learn it?

Data science is all about predictive modelling and you can become an expert in these methods through this 'advance statistics' section.

7. Machine Learning

The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.

Why learn it?

Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.

***What you get***

  • A $1250 data science training program
  • Active Q&A support
  • All the knowledge to get hired as a data scientist
  • A community of data science learners
  • A certificate of completion
  • Access to future updates
  • Solve real-life business cases that will get you the job

You will become a data scientist from scratch

We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.

Why wait? Every day is a missed opportunity.

Click the "Buy Now" button and become a part of our data scientist program today.

Instructor-led, Live & Interactive Sessions

72 Hours
Online (Instructor-led)
Master in Data Science

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How we prepare you

Data Science

Upon completion of this program you will get 360-degree understanding of Data Science. This course will give you thorough learning experience in terms of understanding the concepts, mastering them thoroughly and applying them in real work environment.

Hands-on experience in a live project

You will be given industry level real time projects to work on and it will help you to differentiate yourself with multi-platform fluency, and have real-world experience with the most important tools and platforms.

Unlimited Mock Interview and Quiz Session

As part of this, You would be given complete interview preparations kit, set to be ready for the Machine Learning. This kit has been crafted by 200+ years industry experience and the experiences of nearly 10000 DevOpsSchool's Machine Learning learners worldwide.

Agenda of the Data ScienceDownload Curriculum

  • Part 1: Introduction
  • The Field of Data Science - The Various Data Science Disciplines
  • The Field of Data Science - Connecting the Data Science Disciplines
  • The Field of Data Science - The Benefits of Each Disciplines
  • The Field of Data Science - Popular Data Science Techniques
  • The Field of Data Science - Popular Data Science Tools
  • The Field of Data Science - Careers in Data Science
  • The Field of Data Science - Debunking Common Misconceptions
  • Part 2: Probability
  • Probability - Combinatorics

  • A Practical Example: What You will Learn in This Course
  • What Does the Course Cover
  • Data Science and Business Buzzwords: Why are there so Many?
  • What is the difference between Analysis and Analytics
  • Business Analytics, Data Analytics, and Data Science: An Introduction
  • Continuing with BI, ML, and AI
  • A Breakdown of our Data Science Infographic
  • Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
  • The Reason Behind These Disciplines
  • Techniques for Working with Traditional Data
  • Real Life Examples of Traditional Data
  • Techniques for Working with Big Data
  • Real Life Examples of Big Data
  • Business Intelligence (BI) Techniques
  • Real Life Examples of Business Intelligence (BI)
  • Techniques for Working with Traditional Methods
  • Real Life Examples of Traditional Methods
  • Machine Learning (ML) Techniques
  • Types of Machine Learning
  • Real Life Examples of Machine Learning (ML)
  • Necessary Programming Languages and Software Used in Data Science
  • Finding the Job - What to Expect and What to Look for
  • Debunking Common Misconceptions
  • The Basic Probability Formula
  • Computing Expected Values
  • Frequency
  • Events and Their Complements
  • Fundamentals of Combinatorics
  • Permutations and How to Use Them
  • Simple Operations with Factorials
  • Solving Variations with Repetition
  • Solving Variations without Repetition
  • Solving Combinations
  • Symmetry of Combinations
  • Solving Combinations with Separate Sample Spaces
  • Combinatorics in Real-Life: The Lottery
  • A Recap of Combinatorics
  • A Practical Example of Combinatorics
  • Sets and Events
  • Ways Sets Can Interact
  • Intersection of Sets
  • Union of Sets
  • Mutually Exclusive Sets
  • Dependence and Independence of Sets
  • The Conditional Probability Formula
  • The Law of Total Probability
  • The Additive Rule
  • The Multiplication Law
  • Bayes' Law
  • A Practical Example of Bayesian Inference
  • Fundamentals of Probability Distributions
  • Types of Probability Distributions
  • Characteristics of Discrete Distributions
  • Discrete Distributions: The Uniform Distribution
  • Discrete Distributions: The Bernoulli Distribution
  • Discrete Distributions: The Binomial Distribution
  • Discrete Distributions: The Poisson Distribution
  • Characteristics of Continuous Distributions
  • Continuous Distributions: The Normal Distribution
  • Continuous Distributions: The Standard Normal Distribution
  • Continuous Distributions: The Students' T Distribution
  • Continuous Distributions: The Chi-Squared Distribution
  • Continuous Distributions: The Exponential Distribution
  • Continuous Distributions: The Logistic Distribution
  • A Practical Example of Probability Distributions
  • Probability in Finance
  • Probability in Statistics
  • Probability in Data Science
  • Population and Sample
  • Types of Data
  • Levels of Measurement
  • Categorical Variables - Visualization Techniques
  • Categorical Variables Exercise
  • Numerical Variables - Frequency Distribution Table
  • Numerical Variables Exercise
  • The Histogram
  • Histogram Exercise
  • Cross Tables and Scatter Plots
  • Cross Tables and Scatter Plots Exercise
  • Mean, median and mode
  • Mean, Median and Mode Exercise
  • Skewness
  • Skewness Exercise
  • Variance
  • Variance Exercise
  • Standard Deviation and Coefficient of Variation
  • Standard Deviation
  • Standard Deviation and Coefficient of Variation Exercise
  • Covariance
  • Covariance Exercise
  • Correlation Coefficient
  • Correlation
  • Correlation Coefficient Exercise
  • Practical Example: Descriptive Statistics
  • Practical Example: Descriptive Statistics Exercise
  • Introduction
  • What is a Distribution
  • The Normal Distribution
  • The Standard Normal Distribution
  • The Standard Normal Distribution Exercise
  • Central Limit Theorem
  • Standard error
  • Estimators and Estimates
  • What are Confidence Intervals?
  • Confidence Intervals; Population Variance Known; Z-score
  • Confidence Intervals; Population Variance Known; Z-score; Exercise
  • Confidence Interval Clarifications
  • Student's T Distribution
  • Confidence Intervals; Population Variance Unknown; T-score
  • Confidence Intervals; Population Variance Unknown; T-score; Exercise
  • Margin of Error
  • Confidence intervals. Two means. Dependent samples
  • Confidence intervals. Two means. Dependent samples Exercise
  • Confidence intervals. Two means. Independent Samples (Part 1)
  • Confidence intervals. Two means. Independent Samples (Part 1). Exercise
  • Confidence intervals. Two means. Independent Samples (Part 2)
  • Confidence intervals. Two means. Independent Samples (Part 2). Exercise
  • Confidence intervals. Two means. Independent Samples (Part 3)
  • Practical Example: Inferential Statistics
  • Practical Example: Inferential Statistics Exercise
  • Further Reading on Null and Alternative Hypothesis
  • Null vs Alternative Hypothesis
  • Rejection Region and Significance Level
  • Type I Error and Type II Error
  • Test for the Mean. Population Variance Known
  • Test for the Mean. Population Variance Known Exercise
  • p-value
  • Test for the Mean. Population Variance Unknown
  • Test for the Mean. Population Variance Unknown Exercise
  • Test for the Mean. Dependent Samples
  • Test for the Mean. Dependent Samples Exercise
  • Test for the mean. Independent Samples (Part 1)
  • Test for the mean. Independent Samples (Part 1). Exercise
  • Test for the mean. Independent Samples (Part 2)
  • Test for the mean. Independent Samples (Part 2). Exercise
  • Practical Example: Hypothesis Testing
  • Practical Example: Hypothesis Testing Exercise
  • Introduction to Programming
  • Why Python?
  • Why Jupyter?
  • Installing Python and Jupyter
  • Understanding Jupyter's Interface - the Notebook Dashboard
  • Prerequisites for Coding in the Jupyter Notebooks
  • Jupyter's Interface
  • Python 2 vs Python 3
  • Variables
  • Numbers and Boolean Values in Python
  • Python Strings
  • Using Arithmetic Operators in Python
  • The Double Equality Sign
  • How to Reassign Values
  • Add Comments
  • Understanding Line Continuation
  • Indexing Elements
  • Structuring with Indentation
  • Comparison Operators
  • Logical and Identity Operators
  • The IF Statement
  • The ELSE Statement
  • A Note on Boolean Values
  • Add your content...Defining a Function in Python
  • How to Create a Function with a Parameter
  • Defining a Function in Python - Part II
  • How to Use a Function within a Function
  • Conditional Statements and Functions
  • Functions Containing a Few Arguments
  • Built-in Functions in Python
  • Python Functions
  • Lists
  • Using Methods
  • List Slicing
  • Tuples
  • Dictionaries
  • For Loops
  • While Loops and Incrementing
  • Lists with the range() Function
  • Conditional Statements and Loops
  • Conditional Statements, Functions, and Loops
  • How to Iterate over Dictionaries
  • Object Oriented Programming
  • Modules and Packages
  • What is the Standard Library?
  • Importing Modules in Python
  • Introduction to Regression Analysis
  • The Linear Regression Model
  • Correlation vs Regression
  • Geometrical Representation of the Linear Regression Model
  • Python Packages Installation
  • First Regression in Python
  • First Regression in Python Exercise
  • Using Seaborn for Graphs
  • How to Interpret the Regression Table
  • Decomposition of Variability
  • What is the OLS?
  • R-Squared
  • Multiple Linear Regression
  • Adjusted R-Squared
  • Multiple Linear Regression Exercise
  • Test for Significance of the Model (F-Test)
  • OLS Assumptions
  • A1: Linearity
  • A2: No Endogeneity
  • A3: Normality and Homoscedasticity
  • A4: No Autocorrelation
  • A5: No Multicollinearity
  • Dealing with Categorical Data - Dummy Variables
  • Making Predictions with the Linear Regression
  • What is sklearn and How is it Different from Other Packages
  • How are we Going to Approach this Section?
  • Simple Linear Regression with sklearn
  • Simple Linear Regression with sklearn - A StatsModels-like Summary Table
  • A Note on Normalization
  • Simple Linear Regression with sklearn - Exercise
  • Multiple Linear Regression with sklearn
  • Calculating the Adjusted R-Squared in sklearn
  • Calculating the Adjusted R-Squared in sklearn - Exercise
  • Feature Selection (F-regression)
  • A Note on Calculation of P-values with sklearn
  • Creating a Summary Table with P-values
  • Multiple Linear Regression - Exercise
  • Feature Scaling (Standardization)
  • Feature Selection through Standardization of Weights
  • Predicting with the Standardized Coefficients
  • Feature Scaling (Standardization) - Exercise
  • Underfitting and Overfitting
  • Train - Test Split Explained
  • Practical Example: Linear Regression (Part 1)
  • Practical Example: Linear Regression (Part 2)
  • A Note on Multicollinearity
  • Practical Example: Linear Regression (Part 3)
  • Dummies and Variance Inflation Factor - Exercise
  • Practical Example: Linear Regression (Part 4)
  • Dummy Variables - Exercise
  • Practical Example: Linear Regression (Part 5)
  • Linear Regression - Exercise
  • Introduction to Logistic Regression
  • A Simple Example in Python
  • Logistic vs Logit Function
  • Building a Logistic Regression
  • Building a Logistic Regression - Exercise
  • An Invaluable Coding Tip
  • Understanding Logistic Regression Tables
  • Understanding Logistic Regression Tables - Exercise
  • What do the Odds Actually Mean
  • Binary Predictors in a Logistic Regression
  • Binary Predictors in a Logistic Regression - Exercise
  • Calculating the Accuracy of the Model
  • Underfitting and Overfitting
  • Testing the Model
  • Testing the Model - Exercise
  • Introduction to Cluster Analysis
  • Some Examples of Clusters
  • Difference between Classification and Clustering
  • Math Prerequisites
  • K-Means Clustering
  • A Simple Example of Clustering
  • A Simple Example of Clustering - Exercise
  • Clustering Categorical Data
  • Clustering Categorical Data - Exercise
  • How to Choose the Number of Clusters
  • How to Choose the Number of Clusters - Exercise
  • Pros and Cons of K-Means Clustering
  • To Standardize or not to Standardize
  • Relationship between Clustering and Regression
  • Market Segmentation with Cluster Analysis (Part 1)
  • Market Segmentation with Cluster Analysis (Part 2)
  • How is Clustering Useful?
  • EXERCISE: Species Segmentation with Cluster Analysis (Part 1)
  • EXERCISE: Species Segmentation with Cluster Analysis (Part 2)
  • Types of Clustering
  • Dendrogram
  • Heatmaps
  • What is a Matrix?
  • Scalars and Vectors
  • Linear Algebra and Geometry
  • Arrays in Python - A Convenient Way To Represent Matrices
  • What is a Tensor?
  • Addition and Subtraction of Matrices
  • Errors when Adding Matrices
  • Transpose of a Matrix
  • Dot Product
  • Dot Product of Matrices
  • Why is Linear Algebra Useful?
  • What to Expect from this Part?
  • Introduction to Neural Networks
  • Training the Model
  • Types of Machine Learning
  • The Linear Model (Linear Algebraic Version)
  • The Linear Model
  • The Linear Model with Multiple Inputs
  • The Linear model with Multiple Inputs and Multiple Outputs
  • Graphical Representation of Simple Neural Networks
  • What is the Objective Function?
  • Common Objective Functions: L2-norm Loss
  • Common Objective Functions: Cross-Entropy Loss
  • Optimization Algorithm: 1-Parameter Gradient Descent
  • Optimization Algorithm: n-Parameter Gradient Descent
  • Basic NN Example (Part 1)
  • Basic NN Example (Part 2)
  • Basic NN Example (Part 3)
  • Basic NN Example (Part 4)
  • Basic NN Example Exercises
  • How to Install TensorFlow 2.0
  • TensorFlow Outline and Comparison with Other Libraries
  • TensorFlow 1 vs TensorFlow 2
  • A Note on TensorFlow 2 Syntax
  • Types of File Formats Supporting TensorFlow
  • Outlining the Model with TensorFlow 2
  • Interpreting the Result and Extracting the Weights and Bias
  • Customizing a TensorFlow 2 Model
  • Basic NN with TensorFlow: Exercises
  • What is a Layer?
  • What is a Deep Net?
  • Digging into a Deep Net
  • Non-Linearities and their Purpose
  • Activation Functions
  • Activation Functions: Softmax Activation
  • Backpropagation
  • Backpropagation Picture
  • Backpropagation - A Peek into the Mathematics of Optimization
  • What is Overfitting?
  • Underfitting and Overfitting for Classification
  • What is Validation?
  • Training, Validation, and Test Datasets
  • N-Fold Cross Validation
  • Early Stopping or When to Stop Training
  • What is Initialization?
  • Types of Simple Initializations
  • State-of-the-Art Method - (Xavier) Glorot Initialization
  • Stochastic Gradient Descent
  • Problems with Gradient Descent
  • Momentum
  • Learning Rate Schedules, or How to Choose the Optimal Learning Rate
  • Learning Rate Schedules Visualized
  • Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
  • Adam (Adaptive Moment Estimation)
  • Preprocessing Introduction
  • Types of Basic Preprocessing
  • Standardization
  • Preprocessing Categorical Data
  • Binary and One-Hot Encoding
  • MNIST: The Dataset
  • MNIST: How to Tackle the MNIST
  • MNIST: Importing the Relevant Packages and Loading the Data
  • MNIST: Preprocess the Data - Create a Validation Set and Scale It
  • MNIST: Preprocess the Data - Scale the Test Data - Exercise
  • MNIST: Preprocess the Data - Shuffle and Batch
  • MNIST: Preprocess the Data - Shuffle and Batch - Exercise
  • MNIST: Outline the Model
  • MNIST: Select the Loss and the Optimizer
  • MNIST: Learning
  • MNIST - Exercises
  • MNIST: Testing the Model
  • Business Case: Exploring the Dataset and Identifying Predictors
  • Business Case: Outlining the Solution
  • Business Case: Balancing the Dataset
  • Business Case: Preprocessing the Data
  • Business Case: Preprocessing the Data - Exercise
  • Business Case: Load the Preprocessed Data
  • Business Case: Load the Preprocessed Data - Exercise
  • Business Case: Learning and Interpreting the Result
  • Business Case: Setting an Early Stopping Mechanism
  • Setting an Early Stopping Mechanism - Exercise
  • Business Case: Testing the Model
  • Business Case: Final Exercise
  • Summary on What You've Learned
  • What's Further out there in terms of Machine Learning
  • DeepMind and Deep Learning
  • An overview of CNNs
  • An Overview of RNNs
  • An Overview of non-NN Approaches
  • How to Install TensorFlow 1
  • A Note on Installing Packages in Anaconda
  • TensorFlow Intro
  • Actual Introduction to TensorFlow
  • Types of File Formats, supporting Tensors
  • Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases
  • Basic NN Example with TF: Loss Function and Gradient Descent
  • Basic NN Example with TF: Model Output
  • Basic NN Example with TF Exercises
  • MNIST: What is the MNIST Dataset?
  • MNIST: How to Tackle the MNIST
  • MNIST: Relevant Packages
  • MNIST: Model Outline
  • MNIST: Loss and Optimization Algorithm
  • Calculating the Accuracy of the Model
  • MNIST: Batching and Early Stopping
  • MNIST: Learning
  • MNIST: Results and Testing
  • MNIST: Solutions
  • MNIST: Exercises
  • Business Case: Getting Acquainted with the Dataset
  • Business Case: Outlining the Solution
  • The Importance of Working with a Balanced Dataset
  • Business Case: Preprocessing
  • Business Case: Preprocessing Exercise
  • Creating a Data Provider
  • Business Case: Model Outline
  • Business Case: Optimization
  • Business Case: Interpretation
  • Business Case: Testing the Model
  • Business Case: A Comment on the Homework
  • Business Case: Final Exercise
  • What are Data, Servers, Clients, Requests, and Responses
  • What are Data Connectivity, APIs, and Endpoints?
  • Taking a Closer Look at APIs
  • Communication between Software Products through Text Files
  • Software Integration - Explained
  • Game Plan for this Python, SQL, and Tableau Business Exercise
  • The Business Task
  • Introducing the Data Set
  • What to Expect from the Following Sections?
  • Importing the Absenteeism Data in Python
  • Checking the Content of the Data Set
  • Introduction to Terms with Multiple Meanings
  • What's Regression Analysis - a Quick Refresher
  • Using a Statistical Approach towards the Solution to the Exercise
  • Dropping a Column from a DataFrame in Python
  • EXERCISE - Dropping a Column from a DataFrame in Python
  • SOLUTION - Dropping a Column from a DataFrame in Python
  • Analyzing the Reasons for Absence
  • Obtaining Dummies from a Single Feature
  • EXERCISE - Obtaining Dummies from a Single Feature
  • SOLUTION - Obtaining Dummies from a Single Feature
  • Dropping a Dummy Variable from the Data Set
  • More on Dummy Variables: A Statistical Perspective
  • Classifying the Various Reasons for Absence
  • Using .concat() in Python
  • EXERCISE - Using .concat() in Python
  • SOLUTION - Using .concat() in Python
  • Reordering Columns in a Pandas DataFrame in Python
  • EXERCISE - Reordering Columns in a Pandas DataFrame in Python
  • SOLUTION - Reordering Columns in a Pandas DataFrame in Python
  • Creating Checkpoints while Coding in Jupyter
  • EXERCISE - Creating Checkpoints while Coding in Jupyter
  • SOLUTION - Creating Checkpoints while Coding in Jupyter
  • Analyzing the Dates from the Initial Data Set
  • Extracting the Month Value from the "Date" Column
  • Extracting the Day of the Week from the "Date" Column
  • EXERCISE - Removing the "Date" Column
  • Analyzing Several "Straightforward" Columns for this Exercise
  • Working on "Education", "Children", and "Pets"
  • Final Remarks of this Section
  • A Note on Exporting Your Data as a *.csv File
  • Exploring the Problem with a Machine Learning Mindset
  • Creating the Targets for the Logistic Regression
  • Selecting the Inputs for the Logistic Regression
  • Standardizing the Data
  • Splitting the Data for Training and Testing
  • Fitting the Model and Assessing its Accuracy
  • Creating a Summary Table with the Coefficients and Intercept
  • Interpreting the Coefficients for Our Problem
  • Standardizing only the Numerical Variables (Creating a Custom Scaler)
  • Interpreting the Coefficients of the Logistic Regression
  • Backward Elimination or How to Simplify Your Model
  • Testing the Model We Created
  • Saving the Model and Preparing it for Deployment
  • ARTICLE - A Note on 'pickling'
  • EXERCISE - Saving the Model (and Scaler)
  • Preparing the Deployment of the Model through a Module
  • Are You Sure You're All Set?
  • Deploying the 'absenteeism_module' - Part I
  • Deploying the 'absenteeism_module' - Part II
  • Exporting the Obtained Data Set as a *.csv
  • EXERCISE - Age vs Probability
  • Analyzing Age vs Probability in Tableau
  • EXERCISE - Reasons vs Probability
  • Analyzing Reasons vs Probability in Tableau
  • EXERCISE - Transportation Expense vs Probability
  • Analyzing Transportation Expense vs Probability in Tableau
  • Bonus Lecture: Next Steps

In Data Science Course a Participant will get total 5 real time scenario based projects to work on, as part of these projects, we would help our participant to have first hand experience of real time scenario based software project development planning, coding, deployment, setup and monitoring in production from scratch to end. We would also help our participants to visualize a real development environment, testing environment and production environments.


As part of this, You would be given complete interview preparations kit, set to be ready for the Data Science hotseat. This kit has been crafted by 200+ years industry experience and the experiences of nearly 10000 DevOpsSchool Data Science learners worldwide.


1 Course for Data Science
Faculty Profile Check
Lifetime Technical Support
Lifetime LMS access
Top 46 Tools
Mock Interviews after Training
Training Notes
Step by Step Web Based Tutorials
Training Slides
Training + Additional Videos

The average annual salary of Data Scientists as per Indeed is approximately US$122,801 in the United States

  • Data Scientist is the best job in the 21st century – Harvard Business Review
  • The number of jobs for all data professionals in the United States will increase to 2.7 million – IBM
  • Global Big Data market achieves US$122 billion in sales in 6 years – Frost & Sullivan

The demand for Data Scientists far exceeds the supply. This is a serious problem in a data-driven world that we are living in today. As a result, most organizations are willing to pay high salaries for professionals with appropriate Data Science skills.

Data science training online will help you become proficient in Data Science, R programming language, Data Analysis, Big Data, and more. Thus, you can easily accelerate your career in this evolving domain and take it to the next level.

This course can be signed up by:

  • Information Architects and Statisticians
  • Developers looking to master Machine Learning and Predictive Analytics
  • Big Data, Business Analyst, and Business Intelligence professionals
  • Aspirants looking to work as Machine Learning experts, Data Scientists, etc.

There are no prerequisites for taking up this training course. If you like mathematics, you can accelerate your learning through this Data Scientist course.

In the United States, the average salary of a Data Scientist is US$112,957. The average salary of Data Scientists in India is ₹853,191.

Many top companies hire Data Scientists. A few of them are Amazon, Google, IBM, Facebook, Microsoft, Walmart, Target, Visa, Bank of America, Accenture, Fractal Analytics, etc.

  1. Understand the Problem
  2. Data Scientists should be aware of the business pain points and ask the right questions.

  3. Collect Data
  4. They need to collect enough data to understand the problem in hand and to better solve it in terms of time, money, and resources.

  5. Process the Raw Data
  6. Data is rarely used in its original form. It must be processed, and there are several ways to convert it into a usable format.

  7. Explore the Data
  8. Once the data has been processed and converted into a usable form, Data Scientists must examine it to determine the characteristics and find out obvious trends, correlations, and more.

  9. Analyze the Data
  10. To understand the data, they use a variety of tool libraries, such as Machine Learning, statistics and probability, linear and logistic regression, time series analysis, and more.

  11. Communicate Results
  12. At last, results must be communicated to the right stakeholders, laying the groundwork for all identified issues.

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  • You should take this course if you want to become a Data Scientist or if you want to learn about the field
  • This course is for you if you want a great career
  • The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
  • The course provides the entire toolbox you need to become a data scientist
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Impress interviewers by showing an understanding of the data science field
  • Learn how to pre-process data
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Start coding in Python and learn how to use it for statistical analysis
  • Perform linear and logistic regressions in Python
  • Carry out cluster and factor analysis
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Apply your skills to real-life business cases
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Unfold the power of deep neural networks
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

What are the benefits of "Master in Data Science" Certification?

During this Data Science program, you will be engaged in various projects and assignments, which include real-world industry scenarios. Which will be very helpful to you and you can expedite your career effortlessly.You would be glad to know that our certification training is recognized all around the world.


Because We provide the best Machine Learning training course that gives you all the skills needed to work in the domains of Machine Learning, Deep Learning, Data Science to give the professionals an added advantage. After the completion of the training, you will be awarded the Machine Learning certification. You can know more about us on Web, Twitter, Facebook and linkedin and make your own decision. Also, you can email us to know more about us. We will call you back and help you more about the trusting DevOpsSchool for your online training.

You will have the skills required to help you to land a dream job. Jobs that are ideal for Data Science trained professionals .The demand for machine learning engineers is going to be grown by 60% which is indicated by the increased adoption of Machine learning among companies.

We provide comprehensive teaching in Machine Learning through hands-on projects and case studies. A few of the many reasons for choosing Intellipaat ML training course includes the following:

  • You will learn various concepts such as ML using Python, classification techniques, linear algebra behind linear regression along with logistic regression, supervised and unsupervised learning, and more.
  • After successfully completing the lectures, you will be awarded a certificate, which holds merit in all around the world.
  • We provide lifetime access to videos, resources and their free upgrades to the latest version, and 24/7 learning support.

You will never lose any lecture at DevOpsSchool. There are two options available: You can view the class presentation, notes and class recordings that are available for online viewing 24x7 through our Learning management system (LMS). You can attend the missed session, in any other live batch or in the next batch within 3 months. Please note that, access to the learning materials (including class recordings, presentations, notes, step-bystep-guide etc.)will be available to our participants for lifetime.

We offer the facility of integrated labs that act as a platform for you to execute our industry-based projects. You will be guided through the steps so that you can easily deploy all the necessary tools and further execute the hands-on exercises successfully

After completing this certification training, you will be awarded the certificate from us, which is valid for a lifetime.

We actively provide placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with many MNCs from around the world. We also help you with the job interview and résumé preparation as well.

Data Science is basically the process to collect real-world data, collect useful information from it, and then take actions to perform certain tasks without manual programming. It helps systems improve over time on their own by exploring various types of real-world data which also allows organizations to improve their business strategies by knowing the insights that are extracted from the given business data.

We select instructors who are top SMEs in the industry with a minimum of 8 to12 years of experience in the field of Machine Learning. They are all extremely qualified trainers in the field of Machine Learning and Artificial Intelligence. They are selected after going through a rigorous process, where they are tested for their domain knowledge and training ability.

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  • NEFT or IMPS from all leading Banks
  • Debit card/Credit card
  • Xoom and Paypal (For USD Payments)
  • Through our website payment gateway

If you are reaching to us that means you have a genuine need of this training, but if you feel that the training does not fit to your expectation level, You may share your feedback with trainer and try to resolve the concern. We have no refund policy once the training is confirmed.

Our fees are very competitive. Having said that if the participants are in a group then following discounts can be possible based on the discussion with representative.

  • Two to Three students – 10% Flat discount
  • Four to Six Student – 15% Flat discount
  • Seven & More – 25% Flat Discount

DevOpsSchool provides " Master in Data Science" certificate accredited by which is industry recognized and does holds high value. Participant will be awarded with the certificate on the basis of projects, assignments and evaluation test which they will get within and after the training duration.



Abhinav Gupta, Pune


The training was very useful and interactive. Rajesh helped develop the confidence of all.


Indrayani, India


Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.


Ravi Daur , Noida


Good training session about basic Devops concepts. Working session were also good, howeverproper query resolution was sometimes missed, maybe due to time constraint.


Sumit Kulkarni, Software Engineer


Very well organized training, helped a lot to understand the DevOps concept and detailed related to various tools.Very helpful


Vinayakumar, Project Manager, Bangalore


Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.


Abhinav Gupta, Pune


The training with DevOpsSchool was a good experience. Rajesh was very helping and clear with concepts. The only suggestion is to improve the course content.

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