July 14 2020 0Comment

Data Scientist


This training on data science covers all aspects of data handling, right from ETL to data cleansing and then the major analysis, It also enables you to produce predictive models and visualize data in better ways using graphical ways or dashboards. The training covers a 360-degree view of data science.

Data Scientist Training Institute in Hyderabad

About Data Science

Data is the new fuel and there is no domain in the world of science and technology that cannot be accomplished with the power of data. Data science is valued across all industries, be it the technology sector or the business domains. Business problems require data to reach sensible and profitable decisions.

With ample of theoretical knowledge and data-driven projects, iGlobe Online Training’s Data Science Training will you a head-start into the field of data science. This training will equip you with the important tools and software you need to land yourself into a good data science role.

Why Choose iGlobe Data Science Online Training?

The fact that distinguishes this training from all other contemporaries is that it caters to all the aspects of data science. Right from mathematical concepts, to the areas of machine learning, deep learning, and business analytics, this training covers them all. With iGlobe, you don’t have to refer to any other source to learn any other skill. Ending with a capstone project, you can even have real-time experience of applying your learnings into real problem statements.

With iGlobe Data Science Training, you get to learn elite tools and software that can be profitable for your career ahead. This once-in-a-lifetime opportunity comes with the added advantage of being able to take on diverse projects without the limitation of any industry whatsoever.

Data Science Training Prerequisites:

The course is open to anybody who wishes to step into a new domain which offers ample opportunities for growth. You need not have any prior knowledge of data science; this course will train you from the basics

Data Science Training Objectives:

After completion of Data Science course at IGlobe Online Trainings, you will gain knowledge on:

  • Basic understanding of data science, its importance and applications
  • Mathematics behind data science
  • Applications of data visualization
  • Usage of R, Python and SQL programming
  • Machine Learning algorithms and techniques
  • Deep Learning algorithms
  • Power BI and its usage in data science
  • ML Studio for cloud technology
  • Hands-on capstone project

Data Science Training outcomes:

  • Industry certification on Data Science
  • Ground-level understanding of how data science algorithms work in the analytics world
  • Ability to use data for problem-solving and decision-making in real-world projects
  • Extra edge on the professional projects that require complex understanding of the data involved
  • Access to the industry of business analytics and business intelligence

Data Science Course Curriculum:

Introduction to Data Science

  • What is Data Science?
  • Data Science Life Cycle
  • What is Machine Learning?
  • What is Business Analytics?
  • What is Artificial Intelligence?
  • Data Science vs Machine Learning vs AI
  • Types of Data
  • What is Big Data?
  • Software vs Data vs Cloud
  • Real time applications on Machine Learning


  • Data Types
  • Statistical parameters, variance, standard deviation, range
  • Categorical and Quantitative Data
  • Descriptive Statistics
  • Statistical Inference
  • Sampling and Sampling Distributions
  • Correlation, Covariance and Causation
  • Central Limit Theorem
  • Confidence Interval
  • Hypothesis Testing and Error Types
  • t-test and types of t-test
  • Analysis of Variance (ANOVA)
  • Introduction to Probability
  • Probability Distributions
  • Bernoulli, Uniform, Binomial, Normal Distribution
  • Poisson and Exponential Distribution
  • Skew Normal Distribution
  • Z-Score

Exploratory Data Analysis(EDA) and Data Visualization

  • What is EDA and its Importance
  • Statistical approach (Data Collection, Descriptive statistics, Data Mining)
  • Importing the Data
  • Data Frames
  • Variables, Transformation
  • Standardization and Normalization
  • Validation and Interpretation
  • Distributions
  • Histograms, Outliers
  • Summarizing Distributions
  • Graphs
  • Bar Charts
  • Box-whisker plot
  • Scatter plot
  • Pie Charts
  • Bubble Charts

 Introduction to R Programming

  • Why R and importance of R in Analytics
  • Installation of R and R-studio
  • Working Directories
  • Data Types
  • Operators
  • Loops-For and While
  • If-else statements, Nested statements
  • Objects, and Vectors
  • Strings
  • Arrays
  • Lists
  • Factors
  • Data Frames
  • Pipe Operator
  • Functions (Predefined and User defined) apply, l-apply, s-apply, m-apply, t-apply, v-apply Subset/filter, which, sample, match, sort, mutate, grep, summary,gsub, select, groupby, gather, separate, Posixct
  • Joins (Inner, Outer, Left, Right, Semi, Anti) in Data Frames.
  • Univariate Analysis
  • Dplyr, Lubridate, Tibble

Data manipulation using SQL

  • Introduction to SQL and Data bases
  • SQL developer installation
  • Data types
  • Data types and Operators
  • Create and Drop data base
  • DDL,DML, DCL ,TCL, Sorting commands and other keywords
  • Advanced SQL-Wild cards, Constraints, Joins, Unions, NULL, Alias, Truncate, Views, Sub queries

Introduction to Python Programming

  • What is Python?
  • Importance of Python in Data Science
  • Python Installation Guidelines (Anaconda Navigator)

Python Fundamentals

  • Keywords
  • Built-in functions
  • String Formatting
  • Indexing
  • Slicing
  • Sequences
  • Error handling in Python (try, catch, finally)
  • Ignoring Warnings
  • User-defined functions
  • Nested functions
  • Lambda, zip and map
  • Local and global variables
  • If-else statements, Nested statements
  • Loops, Nested Loops, For, While loop
  • Performance measurement of loops
  • Loop control statements
  • Continue, Break, Pass
  • Class, Constructor and methods

Python Data Structures

  • Lists, Lists Comprehensions
  • Sets
  • Tuple
  • Dictionary
  • Importance of each type

Data Handling with Python

  • Introduction to NumPy, Pandas
  • Arrays and Matrix
  • Importing and exporting datasets in Python
  • Creating Data Frames
  • Data Manipulations
  • Scikit-Learn libraries
  • Data Visualizations in Python
  • Matplotlib, Seaborn, and GGplot
  • Feature Engineering: Feature Selection and Extraction
  • Model Selection
  • Training, Testing and K-Fold cross validation

Introduction to Machine Learning

Machine Learning

  • Types of Machine Learning
  • What is Supervised, Un-Supervised and Reinforcement
  • What are the types of each learning technique?
  • Algorithms used in Machine Learning techniques
  • Difference between Data Science, Machine Learning and AI

Generalized Linear Models (GLM)

  • Introduction to generalized linear models
  • Understanding of Linear and Logistic Regression
  • Underfitting and Overfitting
  • Trade-off between Bias and Variance
  • Regularization techniques (Ridge, Lasso, Elastic-Net Regression)
  • Ordinary Least squares
  • Maximum Likelihood
  • Sigmoid Function
  • Cost Function
  • Gradient Descent
  • One-hot Encoding
  • Label Encoding
  • Model Evaluation metrics
  • Feature Engineering (Features Selection, Extraction)
  • R-Square, Adjusted R-Square, RSME
  • Confusion Matrix
  • Evaluation metrics (Precision, Recall, F-Score, Accuracy)
  • Sensitivity and Specificity
  • ROC-AUC curves
  • Assumptions of Linear Regression
  • Imbalanced Data
  • Sampling issues- Over sampling and Under sampling
  • SMOTE, ADASYN and Near Miss

Decision trees and Random Forests

  • Introduction of Decision Tree and its applications
  • Types of Decision Tree
  • Terminologies in Decision Tree
  • Pros and Cons of Decision Tree
  • CHAID analysis
  • Root nodes Identification
  • Gini Index, Entropy, Chi-Square, Reduction in Variance
  • Solution for overfitting in Decision Tree
  • Tree pruning
  • Hyperparameter tuning
  • Random Search and Grid Search for auto selection of parameters
  • What is Bagging?
  • Introduction to Random Forest and its applications
  • Importance of Random Forest
  • Significant feature selection using Random Forest classifier

                                          Boosting Machines

  • Ensembling Techniques
  • Bagging vs Boosting
  • Gradient Boosting Algorithms
  • Gradient Descent in Boosting Algorithms
  • Gradient Boosting Machines, XGBoost, and AdaBoost
  • Regression and Classification boosting techniques
  • Stacking
  • Pros and Cons of boosting Machines


  • Introduction to clustering
  • K-Means clustering
  • Elbow Method
  • Hierarchical clustering
  • Real time applications

                                               Text Mining

  • Introduction to Text Analytics and Text Mining
  • Introduction to NLP
  • Real time applications
  • Extractingtext from files
  • Data cleaning
  • Introduction to NLTK library
  • Count Vectorizer
  • Understanding of Stopwords and regular expressions
  • Stemming and Lemmatization
  • Word Cloud
  • N-grams
  • Fuzzy String Matching
  • Levenshtein Algorithm
  • Jaro-Winkler Algorithm
  • Cosine Similarity
  • Named Entity Recognition (NER)

K-Nearest Neighbours (KNN)

  • What is KNN and why do we use it?
  • KNN-algorithm and regression
  • Curse of dimensionality and brief introduction to dimension reduction
  • Pros and cons of KNN
  • KNN-outlier treatment and anomaly detection

                                  Naïve Bayes and SVM

  • What is Naïve Bayes
  • Bayes theorem, Conditional Probability
  • Real time applications
  • Pros and cons of Naïve Bayes
  • What is Support Vector Machines (SVM)
  • Training time complexity
  • SVM Classifier
  • Hyperplane, margin and Kernel
  • Hyperparameter tuning
  • Linear and Non-Linear SVM

                               Dimensionality Reduction

  • Introduction to Dimensionality Reduction and its importance
  • Principal Component Analysis (PCA)
  • Kernel PCA
  • Singular Value Decomposition (SVD)
  • Linear Discriminate Analysis (LDA)
  • T-Distributed Stochastic Neighbour Embedding (t-SNE)
  • Applications of Dimensionality Reduction

Time Series Forecasting

  • Introduction to forecasting
  • Data processing and indexing time
  • Time Series & Time Series forecasting
  • Understanding of Stats Models
  • Auto Regressive Integrated Moving Average (ARIMA) model
  • Components: Seasonality, Trend and Noise
  • Autocorrelation
  • Parameter Selection for ARIMA Time series
  • Forecasting and Smoothing methods
  • Forecasts Validation
  • Simple moving average
  • Exponentially weighted moving average

Deep Learning & Neural Networks

  • Introduction to Neural Networks
  • Understanding of ANN
  • Understanding of CNN
  • Understanding of RNN
  • Basic understanding of Feed Forward and Backward propagation
  • Gradient Descent in Neural Networks
  • Stochastic Gradient Descent in Neural Networks
  • Use case of ANN in Python

Data Visualization in PowerBI

  • Introduction to PowerBi
  • Importance of PowerBi
  • PowerBi Architecture
  • Installation of PowerBi Desktop version
  • Data Visualization
  • Slicers
  • PowerBi connection to multiple data sources
  • Bar plots, Pie-Charts and many other
  • Exporting table from the report
  • Publish Workspace
  • Data refresh
  • Scheduling refresh
  • Expose report to web applications
  • Basic and Advanced filtering
  • Data Analysis Expression (DAX) basics

Azure ML Studio

  • What is Azure cloud?
  • Importance of Machine Learning in Cloud.
  • What is Azure ML Studio?
  • What are the components of Azure ML Studio?
  • Experiment/Use case in Azure ML Studio.
  • Deploying the Trained model as a web service.
  • Consumption of Web services

Capstone Project

  • Choose from wide range of projects.
  • Capstone project in Retail Industry
  • Capstone project in Healthcare Industry
  • Capstone project in Finance Industry
  • Capstone project in Insurance Industry
  • Capstone project in Education Industry
  • Capstone project in Ecommerce

Instructor-Led Training Sessions

Learn from Industry Experts

e-learning sessions

Industry specific scenarios

24*7 lifetime support & Access

Flexible schedule

Certification and Job Assistance

1.How is the schedule planned for a particular course?

We plan every course taking into consideration the requirement of every learner. We have a team of excellent instructors who provide you the right training and also discuss the real-time industry scenarios. The sessions are stipulated over a specific period of days but you can access the recorded videos anytime.

2.Where will I get the required course material?

The course material is available in the respective courses. You can get access to the training material immediately as soon as you enroll for a particular course. Also, you don’t need to worry if you need any material in the future as you have a 24*7 lifetime access to the same.

3.What if I miss a class?

No worries! As all our training sessions are recorded you can learn from them whenever and wherever you want. So, even if you miss a class or couldn’t understand any concept, you can go back to the recorded sessions and understand. And our instructors are always available to solve your doubts

4.What are the system and browser requirements for online training?

Most of the requirements differ as per the course training and will be imposed by the Course Management System. Although the minimum requirements are as follows:

  • Processor: Pentium 3.5 GHZ
  • Operating System: Windows 8
  • RAM: 4 GB
  • Hard Drive: 50 GB

Also, it is often recommended to use the latest version of Google Chrome, Firefox, Safari or Internet Explorer as per your system.

Don’t forget to have high-speed internet connectivity!

5.What if I have queries after course completion?

Our team of instructors is 24*7 available to solve all your doubts and queries. You can ask your queries after every session and even after the completion of the training in the future, we will be always available at your help. The training sessions are very much interactive such that you will be able to grasp the knowledge about the course in no time.

6.Do you provide job placement and career assistance?

Yes. We are not just limited to provide course training, but also help to make proper use of it in your career. If you’re not getting the job of your dreams, or having trouble at your current workplace, our job placement assistance is just the perfect place for you. We help students to build a strong job profile and develop the required interview skills too. With us, you’re sure to find the job that will sustain all your needs.

7.How can I make use of your On Job Support?

If you’re looking for any On Job Support for projects or client work, you can connect with us anytime and get the required help. Simply select the IT technology you want support on, choose the required technical training and our professionals will be at your service. Not just that, but we also train you in the field such that next time you face a similar issue, you’ll be able to resolve it on your own.

8.I’m not free during weekdays. How can I complete the course then?

We have special Weekend Training Programs too for learners. We don’t want a single person to miss out on the learning opportunity owing to their hectic schedule and thereby we have crafted this special program.

9.What if I face issues and have more queries?

Our Support Team will be 24*7 at your service. Be it any course related or general queries before and after course completion, we aim to resolve everything.


Data Science is a flourishing field and I had always dream of being a data scientist. With iGlobe Data Science training, I could understand the basics and then move to the advanced concepts easily.


Understanding data and deriving important insights from it is a great skill for all CS engineers today. I am proud to have attended this training.


While taking up the iGlobe sessions, I was surprised to see the in-depth focus on concepts as well as programming in the training.

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