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40,000 35,000

Program Highlights

40 Hours of
Instructor-led Online Training

3 Months
Course Duration

One-on-One
Mentoring Sessions

Extra Classes for
Non-Technical Candidates

10+ Hands-On
Projects

Github Portfolio &
Resume Building

1 Month of
Assured Internship

100% Placement
Assistance

Our Alumni

Ajanth Raghuram

Thank you Xilytica for providing such great training in Data Science & Machine Learning with Python. Must be recommended to all the people who are looking to build a future in Data Science. Great job in covering all the tasks and topics in an easy-to-follow rhythm.

Moulee

First of all, I have to thank Xilytica and my Instructor Nitin for enlightening me about the concepts of Data Science. Here at Xilytica, the Data Science journey lead by Nitin was an awesome one. As a mainframe developer for more than 8+ years, I did not get enough chance

Data Science Independent Consultant (US)

Shridhara K

I had a wonderful experience while learning Data Science from Xilytica. Our trainer Nitin took the time to clear all the concepts clearly. I find the training really informative and descriptive and to the point. I liked the Instructor-led Online training approach as it helps to completely cover topics without

B Layek

I really enjoyed the data science classes with Nitin. The good thing is he made me understand the essence of data science in the present day through his training classes and how we can use all the tools to get the best out of the data available. The best thing

Tata Consulting Services (US)

Abhay Zaqui

The classes were well-organized and quite informative as we were always being taught with real datasets & real-life examples. What helped even more in a better understanding of concepts were the case studies and the project, in which we had to apply all the theory. Helped us solve real-time problems.

Capgemini

Katyayini Cherukumudi

Recently completed Data Science course; Trainer Nitin is really good in the way he taught with real-time examples. The way he provided training is completely different from other institutes. I strongly recommend joining this course to whoever is looking to learn Data Science as it is really worth it.

Capgemini

Nagendra Hunsur

Xilytica is a great place for developing a foundation in Analytics. The faculty has super knowledge & a good grasp of Analytics and the study material, case studies, assignments & projects are easy to understand & to practice. Students are provided timely guidance and classes at convenient times. It was

Srinath

I joined Xilytica after doing a lot of research as I was looking for an institute where I can learn AI/ML not only a job or knowledge but also where I can get a direction on how to start a start-up in the field of analytics and machine learnings. I

TCS

Geethanjali Twarakavi

I am very pleased with the Data Science training. When I started I had no knowledge of reporting and now I feel very confident in the area. The exercises given were very helpful and it was ensured that the subject and concepts were well understood. I highly recommend Xilytica for

Tracxn

Our Alumni Work at

Data Science @ Xilytica

  • Scope Of Data Analyst
  • Course Objective
  • Course Is For
  • Course Prerequisites

The Data Analytics and Data Science industry is growing multi-fold each year and is already revolutionising the way business is being done. Data is more abundant and accessible than ever in today’s business environment. We create 2.5 Quintillion bytes of data each day that is 2.5 Billion Gigabytes of data each day. With the increasing amount of data, organizations & business leaders want to hire data-savvy professionals who can analyze & get actionable business insights from this astronomical amount of data. Organizations view data analysis as one of the most crucial skills due to the value that it creates to the businesses. Professionals who enter Data Analytics enjoy lucrative salaries. According to an IBM report, data and analytics jobs are predicted to touch 2.72 million jobs by 2020 with a $67,900 average entry-level salary.

Learning path in this Data Analyst & Business Intelligence program is specifically designed to give you the tools that you need to be successful in the field of Data Analysis & Data Visualisation.  By the end of this Data Analyst & BI training, you will:

  • Understand essential statistical concepts including measures of central tendency, dispersion and  correlation
  • Understand  Databases & Master SQL concepts & Queries
  • Learn & implement Python concepts.
  • Learn how to interpret data in Python using multi-dimensional arrays in NumPy, manipulate DataFrames in pandas
  • Perform data analytics using popular Python libraries
  • Gain insights on several data visualization libraries in Python; including Matplotlib, and Seaborn
  • Present information in the form of metrics, KPIs, reports, and dashboards
  • Learn and create an impressive dashboard using Tableau, showcasing business insights in the form of different chart types.
  • IT professionals looking for career or technology change to Data  Analytics & Data Science
  • BPO industry or Non Technical professionals who are looking for career in Data  Analytics & Data Science 
  • Engineering graduates who want to build a career in Data Science & Analytics
  • Non- technical & MBA graduates who are interested in building a career in Data Science
  • Anyone who likes manipulating data & loves getting insights out of data
  • No prior programming experience is required. We will start from the very basics & gradually take to advanced level
  • You’ll need to install Anaconda, MySQL, Workbench, Tableau Public. We will show you how to do that step by step
     

Curriculum For Certification In
Data Science

Introduction To Data Science & Machine Learning

  • What is Data Science & Machine Learning
  • Importance of Data Science & Machine Learning
  • Demand for Data Science Professional
  • The lifecycle of the Data Science & Machine learning Project
  • Tools and Technologies used in Data Science

 

Introduction To Python Programming

  •  Introduction To The Course
  •  Environment Set Up
  •  Overview Of Anaconda, Jupyter notebook &  Spyder
  •  Installation Set-up
  •  Python Objects & Data Structure Basics – Numbers, Strings, Boolean
  •  Introduction To List
  •  Introduction To Dictionaries
  •  Introduction To Tuples
  •  Introduction To Sets
  •  Comparison Operators
  •  If, Elif & Else Statements
  •  For Loop
  •  While Loop
  •  Functions & Methods
  •  Data Analysis Using Numpy
  • Data Analysis Using Pandas

Mathematics & Linear Algebra

  • Vectors & Matrices
  • Matrix Operations
  • Eigenvectors & Eigenvalues

 

Introduction To Descriptive Statistics 

  • Types of Data
  • Basic Plots Types
  • Mean, Mode & Median
  • Skewness
  • Variance
  • Standard Deviation
  • Covariance
  • Correlation
  • Histogram
  • Boxplot
  • Scatter Plot
  • Bar Chart

 

Introduction To Inferential Statistics

  • What is Inferential Statistics
  • Sample Vs Population
  • Standard Normal Distribution
  • Central Limit Theorem
  • Confidence Interval

Exploratory Data Analysis 

  • Introduction to EDA
  • Data Sourcing & Data cleaning
  • Data Manipulation
  • Dealing With Missing Values
  • Outliers treatment
  • Types of variables
  • Univariate Analysis on Unordered, ordered and quantitative variables
  • Rank-Frequency and Power Law distribution
  • Bivariate Analysis
  • Correlation

Data Visualisation

  • Data Visualization Using Matplotlib & Seaborn
  • Introduction to Matplotlib & Seaborn
  • Basic plotting
  • Figures and sub plotting
  • Box plot, Histograms, Scatter plots, image loading

Introduction To Machine Learning With Python

  • Types Of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Regression Models
  • Classification Model

Data Preprocessing

  • Feature Scaling Data
  • Splitting Data Into Training & Validation & Test Sets
  • Dealing With Categorical Data
  • Dealing With Nulls 
  • Dealing With Outliers
  • K-Fold Cross Validation
  • Model Fit
  • Simple & Complexity of a Model

Supervised Learning

Linear Regression

  • Introduction to Regression Models
  • Correlation Between Two Variables
  • Visualizing Correlation Using Corrgram And Corrplot
  • simple linear regression
  • Multiple Linear Regression
  • Training & Evaluating Regression Models Performance
  • The cost function, R-Square, MSE, RMSE & best-fit line
  • Interpretation Of Regression Plots - Residual Vs Fitted Values
  • Q-q Plot, Scale Location Plot, Residuals Vs Leverage Plot
  • Multi-collinearity
  • Adjusted R-Square, P-Value, and VIF
  • Understanding Heteroscedasticity
  • Overfitting
  • Regularisation
  • Ridge Regression
  • Lasso Regression
  • Projects on Linear, Ridge & Lasso Regression

Classification Models

  • Introduction To Classification Model
  • Training And Evaluating Classification Models
  • Confusion Matrix - Accuracy, Precision Etc
  • Interpreting Roc Curve
  • Fine Tuning Models Using Hyper Parameters

Logistic Regression

  • Introduction To Logistic Regression
  • Sigmoid function & Log of odds
  • Threshold Value
  • Building the Logistic Regression Model
  • Visualizing Logistic Regression
  • Interpreting Logistic Regression
  • Making Probabilistic Predictions
  • Application of Logistic Regression to Multi-Class Classification
  • Advantages and Disadvantages of Logistic Regression
  • Project On Logistic Regression

Decision Tree

  • Types Of Tree-Based Models
  • Introduction To Decision Tree
  • Understanding CART Model Classification Rules
  • Information Gain, Entropy Gain, Gini Index
  • Building Decision Tree Model
  • Visualizing Decision Tree
  • Interpreting Decision Tree
  • Making Predictions
  • Decision Tree Case Study
  • Project On Decision Tree

Random Forests

  • Introduction To Random Forest
  • Ensembles techniques - Bagging & Boosters 
  • Advantages of Random Forest over Decision Trees 
  • Building a Random Forest Model
  • Visualizing Random Forest
  • Interpreting Random Forest
  • Making Predictions
  • Fine Tuning Random Forest Using Hyper Parameters
  • Variable Selection And Variable Importance Plot
  • Random Forest Case Study
  • Project On Random Forest

Support Vector Machines

  • Introduction To Support Vector Machines
  • Hyperplane & Linear discriminator 
  • Building Support Vector Model
  • Understanding Kernel And Gamma In Svm
  • Visualizing Support Vector Machine
  • Interpreting Support Vector Machine
  • Making Predictions
  • Boundary & Feature transformation 
  • Kernel Tricks 
  • Handling non-linearity in the dataset using various Kernels 
  • Fine Tuning Support Vector Machine
  • Support Vector Machine Case Study
  • Project On Support Vector Machine

K Nearest Neighbour

  • Introduction To K Nearest Neighbour
  • Building K Nearest Neighbour Model
  • Visualising K Nearest Neighbour
  • Interpreting K Nearest Neighbour
  • Making Predictions
  • K Nearest Neighbour Case Study
  • Project On K Nearest Neighbour

Unsupervised Learning 

  •  

K - Means Clustering

  • Understanding K – Means Clustering
  • Selecting Right Number Of Clusters
  • Elbow Plot
  • K Means Clustering Case Study
  • Project On K Means Clustering

 

Portfolio Building

  • Introduction to Git & Github 
  • Resume Building
  • Portfolio Building With 10 Plus Projects

  • Introduction To Relational Database
  • Database Fundamentals
  • Creating Table
  • Add, Rename & Drop Columns
  • Insert Data To Table
  • Drop-Table
  • Database Constraints
  • Select Statement
  • Select Distinct
  • Filtering Rows
  • Where Clause
  • OR, Between, IN
  • NULL & IS NULL
  • LIKE & NOT LIKE
  • Aggregate Functions
  • Group By
  • Having Clause
  • Order By
  • Inner Join
  • Full Outer Join
  • Left Join
  • Right Join
  • Union
  • Inbuilt Functions
  • Conditional Expressions

Nitin has over 15 years of rich experience across IT & Analytics technology stack. He has worked for companies like Xerox, Wipro Technologies & have worked across different geographies like India, US & Australia, cutting across different domains like banking, supply chain, health care, e-commerce etc. Nitin has also trained 1000 plus candidates in the field of Data Science & Machine Learning. He likes exploring and solving challenging and competitive industrial problems in the field of Analytics, NLP, & Deep Learning, and converting them into actionable insights. Passionate about building scalable, high-quality practical data-driven solutions and likes to talk about futuristic ideas and product engineering. He has led several data science teams while pairing with machine learning researchers and engineers to maximise the speed of experimentation and production. His interests include Machine Learning, Image Processing, Computer Vision, Deep Learning and he has a strong understanding of analytics in the retail, marketing, and finance areas. 

Our Data Science Experts are always available to take your query​!

+91 72590 78678

    • Program Features
    • Career Support
    • Data Analyst Job Stats

    Program Features

    Join any batch based on your availability

    Extra classes for Non-Technical candidates

    One-on-One Mentoring & Career Guidance

    Job-specific Interview Preparation

    Career Support

    Portfolio Building

    Building Resume

    Interview Preparation

    Assured Internship

    Personalized mentorship

    Support After Training

    Data Science Job Stats

    As Per IBM report, Data & Analytics Jjobs are predicted to touch 2.5 million by 2020

    As Per Glassdoor, Entry Level Data Analyst's Salary In India is Rs 5.0 Lacs/ Year​

    As per Glassdoor, Data Analyst's Salary in US is $67,900/ Year

    Analytics Insight predicts 3 Million Job opening in Data Science in 2021 worldwide

    Fundamental & Industry Projects

    Tools We Use

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