Data Science Masters Program

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Data Science
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Enroll now to become a Certified Data Science expert with EDTIA Data Science Masters Program and upgrade your skills.

Course Description

Data Science Masters Program makes you experienced in tools and systems utilized by Data Science Professionals. It contains training in Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow, and Tableau.

Data Science Masters Program makes you skilled in tools and systems operated by Data Science Professionals. It possesses training in Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow, and Tableau.

A data science master's program helps you acquire skills to collect, manage and analyze data, its types, trends, and deliver the results accordingly. This advanced skill set is spread out throughout the M. Sc.

There are no prerequisites for enrollment in the Data Science Masters Program.

experienced professional working in the IT industry, an aspirant preparing to join the world of Data Science

Today, companies across multiple industries operate and rely on data scientists to develop their businesses. Data scientists are generally responsible for collecting and analyzing raw data, using data to gain insights into business processes to help achieve various goals.

Data scientists study which queries need responding to and where to locate the related data. They have business acumen and analytical skills and can mine, clean, and present data. Businesses utilize data scientists to source, manage, and analyze large amounts of unstructured data.

As companies hope to extract essential insights from big data, the demand for data scientists is on a consistent rise. Reports suggest that India is the second-highest country after the US created the requirement to recruit around 50,000 data scientists in 2020 and 2021.

Data scientists examine which questions need answering and where to discover the corresponding data. They have business acumen and analytical skills and can mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.

What you'll learn

  • In this course, you will learn: Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau

Requirements

  • There are requirements for learning this course.

Curriculam

Understand data and its types accordingly sample data and derive meaningful information from the data in different statistical parameters.

Introduction to Data Types
Numerical parameters to represent data
Mean,
Mode,
Median,
Sensitivity,
Information Gain,
Entropy,
Statistical parameters to represent data,
Estimating mean, median, and mode using PythonPython,
Calculating Information Gain and Entropy,

learn about probability, interpret & solve real-life problems using chance. You will get to know the power of possibility with Bayesian Inference.

Uses of probability,
Need of probability,
Bayesian Inference,
Density Concepts,
Normal Distribution Curve,
Calculating probability using Python,
Conditional, Joint, and Marginal Probability using PythonPython,
Plotting a Normal distribution curve

Illustrate inferences from present data and construct predictive models utilizing various inferential parameters (as a constraint)

Point Estimation,
Confidence Margin,
Hypothesis Testing,
Levels of Hypothesis Testing,
Calculating and generalizing point estimates using PythonPython,
Analysis of Confidence Intervals and Margin of Error

understand the various methods of testing the alternative hypothesis.

Parametric Test,
Parametric Test Types,
Non- Parametric Test,
Experimental Designing,
A/B testing,
Perform p test and t-tests in PythonPython,
A/B testing in PythonPython

introduction to Clustering as part of this Module which forms the basis for machine learning.

Association and Dependence,
Causation and Correlation,
Covariance,
Simpson's Paradox,
Clustering Techniques,
Correlation and Covariance in PythonPython,
Hierarchical Clustering in PythonPython,
K means Clustering in PythonPython

Discover the roots of Regression Modelling operating statistics.

Logistic and Regression Techniques,
Problem of Collinearity,
WOE and IV,
Residual Analysis,
Heteroscedasticity,
Homoscedasticity,
Perform Linear and Logistic Regression in PythonPython,
Analyze the residuals using PythonPython

learn data and its types and will accordingly sample data and derive meaningful information from the data in terms of different statistical parameters

Introduction to Data Types,
Numerical parameters to represent data,
Mean,
Mode,
Median,
Sensitivity,
Information Gain,
Entropy,
Statistical parameters to represent data,
Estimating mean, median, and mode using R,
Calculating Information Gain and Entropy

learn about probability, interpret & solve real-life problems using chance. You will get to know the power of possibility with Bayesian Inference.

Uses of probability,
Need of probability,
Bayesian Inference,
Density Concepts,
Normal Distribution Curve,
Calculating probability using R,
Conditional, Joint, and Marginal Probability using R,
Plotting a Normal distribution curve

learn to draw inferences from present data and construct predictive models using different inferential parameters (as the constraint).

Point Estimation,
Confidence Margin,
Hypothesis Testing,
Levels of Hypothesis Testing,
Calculating and generalizing point estimates using R,
Analysis of Confidence Intervals and Margin of Error

understand the various methods of testing the alternative hypothesis.

Parametric Test,
Parametric Test Types,
Non- Parametric Test,
A/B testing,
Perform P test and T-tests in R

get an introduction to Clustering, which forms the basis for machine learning.

Association and Dependence,
Causation and Correlation,
Covariance,
Simpson's Paradox,
Clustering Techniques,
Correlation and Covariance in R,
Hierarchical Clustering in R,
K means Clustering in R

know about the roots of Regression Modelling operating statistics.

Logistic and Regression Techniques,
Problem of Collinearity,
WOE and IV,
Residual Analysis,
Heteroscedasticity,
Homoscedasticity,
Perform Linear and Logistic Regression in R,
Analyze the residuals using R,
Calculation of WOE values using R

introduction to Data Science and see how Data Science helps to analyze large and unstructured data with various tools.

What is Data Science?
What does Data Science involve?
The era of Data Science,
Business Intelligence vs. Data Science,
The life cycle of Data Science,
Tools of Data Science,
Introduction to Big Data and Hadoop,
Introduction to R,
Introduction to Spark,
Introduction to Machine Learning

know about various statistical techniques and terminologies utilized in data analysis.

What is Statistical Inference?
Terminologies of Statistics,
Measures of Centers,
Measures of Spread,
Probability,
Normal Distribution,
Binary Distribution

Discuss the various sources available to extract data, organize the data in a structured form, examine the data, and represent the data in a graphical format.

Data Analysis Pipeline,
What is Data Extraction,
Types of Data,
Raw and Processed Data,
Data Wrangling,
Exploratory Data Analysis,
Visualization of Data,
Loading various types of the dataset in R,
Arranging the Data,
Plotting the graphs

introduction to Machine Learning, discuss the different categories of Machine Learning and execute Supervised Learning Algorithms.

What is Machine Learning?
Machine Learning Use-Cases,
Machine Learning Process Flow,
Machine Learning Categories,
Supervised Learning algorithm (Linear Regression and Logistic Regression),
Implementing the Linear Regression model in R,
Implementing the Logistic Regression model in R

learn the Supervised Learning Techniques and implement various techniques, such as Decision Trees, Random Forest Classifier, etc.

What are Classification and its use cases?
What is a Decision Tree?
Algorithm for Decision Tree Induction,
Creating a Perfect Decision Tree,
Confusion Matrix,
What is Random Forest?
What is Naive Bayes?
Support Vector Machine: Classification,
Implementing the Decision Tree model in R,
Implementing Linear Random Forest in R,
Implementing the Naive Bayes model in R,
Implementing Support Vector Machine in R

Know about Unsupervised Learning and the different types of Clustering that can be utilized to analyze the data.

What is Clustering & its use cases,
What is K-means Clustering?
What is C-means Clustering?
What is Canopy Clustering?
What is Hierarchical Clustering?
Implementing K-means Clustering in R,
Implementing C-means Clustering in R,
Implementing Hierarchical Clustering in R

know about association rules and various types of Recommender Engines.

What are Association Rules & their use cases?
Recommendation Engine & Is it working?
Types of Recommendations,
User-Based Recommendation,
Item-Based Recommendation,
Difference: User-Based and Item-Based Recommendation,
Recommendation use cases,
Implementing Association Rules in R,
Building a Recommendation Engine in R

Examine Unsupervised Machine Learning Techniques and the execution of various algorithms, for example, TF-IDF and Cosine Similarity.

The concepts of text-mining,
Use cases,
Text Mining Algorithms,
Quantifying text,
TF-IDF,
Beyond TF-IDF,
Executing the Bag of Words approach in R,
Running Sentiment Analysis on Twitter Data using R

learn about Time Series data, different components of Time Series data, Time Series modeling - Exponential Smoothing models, and ARIMA model for Time Series Forecasting.

What is Time Series Data?
Time Series variables,
Different components of Time Series data,
Visualize the data to determine Time Series Components,
Implement the ARIMA model for Forecasting,
Exponential smoothing models,
Identifying different time series scenarios based on which other Exponential Smoothing models can be applied,
Implement respective ETS models for Forecasting,
Visualizing and formatting Time Series data,
Plotting decomposed Time Series data plot,
Utilizing ARIMA and ETS model for Time Series Forecasting,
Forecasting for the given Period

Intro to Reinforcement Learning and Deep Learning concepts, discuss Artificial Neural networks, the building blocks for Artificial Neural Networks, and a few Artificial Neural Network terminologies.

Reinforced Learning,
Reinforcement learning Process Flow,
Reinforced Learning Use cases,
Deep Learning,
Biological Neural Networks,
Understand Artificial Neural Networks,
Building an Artificial Neural Network,
How ANN works,
Important Terminologies of ANN's

get a brief idea of it and learn the basics.

Overview,
The Companies using PythonPython,
Different Applications where it is used,
Discuss Python Scripts on UNIX/Windows,
Values, Types, Variables,
Operands and Expressions,
Conditional Statements,
Loops,
Command Line Arguments,
Writing to the screen,
Creating "Hello World" code,
Variables,
Demonstrating Conditional Statements,
Demonstrating Loops
Skills: Fundamentals of Python programming

understand various types of sequence structures, related operations, and their usage. Understand diverse ways of opening, reading, and writing to files.

Python files I/O Functions,
Numbers,
Strings and related operations,
Tuples and related operations,
Lists and related operations,
Dictionaries and related operations,
Sets and related operations,
Tuple - properties, associated processes, compared with a list,
List - properties, related operations,
Dictionary - properties, related operations,
Set - properties, related operations
Skills: File Operations using PythonPython, Working with data types of Python

learn how to create generic scripts, address errors/exceptions in code, and finally, how to extract/filter content using Regex.

Functions,
Function Parameters,
Global Variables,
Variable Scope and Returning Values,
Lambda Functions,
Object-Oriented Concepts,
Standard Libraries,
Modules Used in Python,
The Import Statements,
Module Search Path,
Package Installation Ways,
Errors and Exception Handling,
Handling Multiple Exceptions,
Functions (Syntax, Arguments, Keyword Arguments, Return Values),
Lambda ( Features, Syntax, Options, Compared with the Functions),
Sorting (Sequences, Dictionaries, Limitations of Sorting),
Errors and Exceptions (Types of Issues, Remediation),
Packages and Module (Modules, Import Options, sys Path)
Skills: Error and Exception management in PythonPython, Working with functions in Python

understand the basics of statistics, different measures and probability distributions, and the supporting libraries in these operations.

NumPy - arrays,
Operations on arrays,
Indexing, slicing and iterating,
Reading and writing arrays on files,
Pandas - data structures & index operations,
Reading and Reporting data from Excel/CSV formats into Pandas,
matplotlib library,
Grids, axes, plots,
Markers, colors, fonts, and styling,
Types of plots ( bar graphs, pie charts, histograms),
Contour plots,
NumPy library (making NumPy array, operations performed on NumPy array),
Pandas library(developing series and data frames, Importing and exporting Data),
Matplotlib (operating Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot)
Skills: Probability Distributions in Python, Python for Data Visualization

understand in detail about Data Manipulation.

Basic Functionalities of a data object,
Merging of Data objects,
Concatenation of data objects,
Types of Joins on data objects,
Exploring a Dataset,
Analysing a dataset,
Pandas Function - Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(),
GroupBy operations ,
Aggregation ,
Concatenation ,
Merging ,
Joining,
Skills: Python in Data Manipulation

Know the concept of Machine Learning and its types.

Revision (numpy, Pandas, scikit learn, matplotlib),
What is Machine Learning?
Machine Learning Use-Cases,
Machine Learning Process Flow,
Machine Learning Categories,
Linear regression,
Gradient descent,
Linear Regression – Boston Dataset
Skills: Machine Learning concepts, Machine Learning types, Linear Regression Implementation

Understand Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest classifiers, etc.

What are Classification and its use cases?
What is a Decision Tree?
Algorithm for Decision Tree Induction,
Creating a Perfect Decision Tree,
Confusion Matrix,
What is Random Forest?
Implementation of Logistic regression,
Decision tree, Random forest
Skills: Supervised Learning concepts, Implementing different types of Supervised Learning algorithms, Evaluating model output

learn about the impact of dimensions within data, perform factor analysis using PCA and compress sizes, developing an LDA model.

Introduction to Dimensionality,
Why Dimensionality Reduction,
PCA,
Factor Analysis,
Scaling dimensional model,
LDA,
PCA,
Scaling
Skills: Implementing Dimensionality Reduction Technique

Understand Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest classifiers, etc.

What is Naïve Bayes?
How Naïve Bayes works?
Implementing Naïve Bayes Classifier,
What is a Support Vector Machine?
Illustrate how Support Vector Machine works?
Hyperparameter Optimization,
Grid Search vs. Random Search,
Implementation of Support Vector Machine for Classification,
Implementation of Naïve Bayes, SVM
Skills: Supervised Learning concepts, Implementing different types of Supervised Learning algorithms, Evaluating model output

Know about Unsupervised Learning and the various types of Clustering that can be utilized to analyze the data.

What is Clustering & its Use Cases?
What is K-means Clustering?
How does the K-means algorithm work?
How to do optimal Clustering,
What is C-means Clustering?
What is Hierarchical Clustering?
How does Hierarchical Clustering work?
Implementing K-means Clustering,
Implementing Hierarchical Clustering,
Skills: Unsupervised Learning, Implementation of Clustering – various types

know Association rules and their extension towards recommendation engines with the Apriori algorithm.

What are Association Rules?
Association Rule Parameters,
Calculating Association Rule Parameters
Recommendation Engines,
How do Recommendation Engines work?
Collaborative Filtering,
Content-Based Filtering,
Apriori Algorithm,
Market Basket Analysis
Skills: Data Mining using PythonPython, Recommender Systems using PythonPython

learn about developing an intelligent learning algorithm such that the Learning becomes more and more accurate as time passes by.

What is Reinforcement Learning,
Why Reinforcement Learning,
Elements of Reinforcement Learning,
Exploration vs. Exploitation dilemma,
Epsilon Greedy Algorithm,
Markov Decision Process (MDP),
Q values and V values,
Q – Learning,