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Master Program in Data Science

The Master program in data science trains you in the tools and systems used by data science professionals. It includes training in statistics, data science, Python, Apache Spark, and Scala, Tensorflow and Tableau. The curriculum was determined by extensive research on more than 5,000 job descriptions around the world.
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876 Ratings
1254 Learners
35 Hours
15 Hours
Master Program
365 Days
24 / 7 Support
Intermediate

About Course

From the gangboard, Master Data Scientist program will help you master skills and tools like statistics, hypothesis testing, grouping, decision trees, linear regression and logistic, R Studio, data visualization, regression models, Hadoop, Spark, SQL PROC, SAS macros, statistical procedures, tools and analysis and more. The course also covers a final project covering all key aspects of data extraction, cleaning, visualization, construction, and model fitting. These skills will help you prepare for the role of a data scientist.

The program provides access to high-quality content e-learning, simulation tests, a moderated community experts and other resources to ensure that follow the ideal path to the dream role than scientific data.

A data scientist is a primary category in an analysis organization. Glassdoor ranked the data scientist among the top 25 jobs by 2016, and good data scientists are in short supply and in high demand. Like scientific data, you will be asked to understand the business problem, project analysis, collect and format required data, apply algorithms or techniques using the right tools, and finally make recommendations supported by data.

Works that are ideal for professionals trained in data science include:
  • Specialist in statistical programming
  • Data Analyst
  • Data scientist
  • Data Science Manager
The role of data science requires the seamless fusion of experience, knowledge of data science, and the use of the right tools and technologies. It is a good career choice for new and experienced professionals. Aspiring professionals of any educational level with an analytical mood are best suited to continue with the Data Scientist Masters program, which includes:
  • IT Professionals
  • Analytical managers
  • Business analysts
  • Banking and financial professionals.
  • Marketing Managers
  • Supply Chain Managers
  • Those new to the data analysis domain
  • Students in UG / PG Analytics programs

The degree of graduation is not enough: Human Resources experts believe that graduates will now have to obtain a specialized master's degree to get a job. Since the curriculum is outdated and the number of graduates is enormous, students must learn some specific skills with practical experience to differentiate themselves from the crowd.

Data Science works in high demand: The number of new jobs in Data Science and Analytics announced per month increased by 78% between April 2017 and April 2018. Job functions such as business analysts, data analysts, data engineers, engineers, and analysis are in high demand through leading companies.

Embark on a rewarding career: Average salaries for the most recent papers in science and data analysis range from 5 to 7 times per year. In addition, annual growth is higher in analysis functions, compared to generic functions, as industry analysis is growing by 36%, while IT industry growth is 6%.

Program Highlights

  • Classroom format
  • World-class teachers and industry experts
  • Corporate Collaboration
  • Practical Learning
  • Placement assistance

Syllabus of Data Science Master Program

Statistics Essentials for Analytics

All the topics in the following section will explain the basis of what it is, which scenario you want to use, What math behind it, How to implement with an analytic tool, what inferences you are getting from the final result.

  • Understanding the Data
  • Probability and its Uses
  • Statistical Inference
  • Data Clustering
  • Testing the Data
  • Regression Modelling
 

Data Science With R

Module 1: Introduction to R (Duration: 2Hrs)

  • What is R?
  • Why R?
  • Installing R
  • R environment
  • How to get help in R
  • R Studio Overview

Module 2: R Basics (Duration: 5Hrs)

  • Environment setup
  • Data Types
  • Variables
  • Vectors
  • Lists
  • Matrix
  • Array
  • Factors
  • Data Frames
  • Loops
  • Packages
  • Functions
  • In-Built Data sets

Module 3: R Packages (Duration: 3Hrs)

  • DMwR
  • Dplyr/plyr
  • Caret
  • Lubridate
  • E1071
  • Cluster/fpc
  • table
  • Stats/utils
  • Ggplot/ggplot2
  • Glmnet

Module 4: Machine Learning using R (Duration: 10Hrs)

  • Linear Regression
  • Logistic Regression
  • K-Means
  • K-Means++
  • Hierarchical Clustering - Agglomerative
  • CART
  • c5.0
  • Random forest
  • Naïve Bayes
 

Data Science With Python

Module 1: Introduction to Data Science (Duration: 1Hr)

  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & it’s types

Module 2: Introduction to Python (Duration: 1Hr)

  • What is Python?
  • Why Python?
  • Installing Python
  • Python IDEs
  • Jupyter Notebook Overview

Module 3: Python Basics (Duration: 5Hrs)

  • Python Basic Data types
  • Lists
  • Slicing
  • IF statements
  • Loops
  • Dictionaries
  • Tuples
  • Functions
  • Array
  • Selection by position & Labels

Module 4: Python Packages (Duration: 2Hrs)

  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library

Module 5: Importing data (Duration: 1Hr)

  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to csv file

Module 6: Manipulating Data (Duration: 1Hr)

  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques

Module 7: Statistics Basics (Duration: 11Hrs)

  • Central Tendency
    • Mean
    • Median
    • Mode
    • Skewness
    • Normal Distribution
  • Probability Basics
    • What does mean by probability?
    • Types of Probability
    • ODDS Ratio?
  • Standard Deviation
    • Data deviation & distribution
    • Variance
  • Bias variance Trade off
    • Underfitting
    • Overfitting
  • Distance metrics
    • Euclidean Distance
    • Manhattan Distance
  • Outlier analysis
    • What is an Outlier?
    • Inter Quartile Range
    • Box & whisker plot
    • Upper Whisker
    • Lower Whisker
    • Scatter plot
    • Cook’s Distance
  • Missing Value treatments
    • What is a NA?
    • Central Imputation
    • KNN imputation
    • Dummification
  • Correlation
    • Pearson correlation
    • Positive & Negative correlation

Module 8: Error Metrics (Duration: 3Hrs)

  • Classification
    • Confusion Matrix
    • Precision
    • Recall
    • Specificity
    • F1 Score
  • Regression
    • MSE
    • RMSE
    • MAPE

Machine Learning

Module 9: Supervised Learning (Duration: 6Hrs)

  • Linear Regression
    • Linear Equation
    • Slope
    • Intercept
    • R square value
  • Logistic regression
    • ODDS ratio
    • Probability of success
    • Probability of failure
    • ROC curve
    • Bias Variance Tradeoff

Module 10: Unsupervised Learning (Duration: 4Hrs)

  • K-Means
  • K-Means ++
  • Hierarchical Clustering

Module 11: Other Machine Learning algorithms (Duration: 10Hrs)

  • K – Nearest Neighbour
  • Naïve Bayes Classifier
  • Decision Tree – CART
  • Decision Tree – C50
  • Random Forest
 

Tableau

Module 1: Tableau Course Material (Duration: 5Hrs)

  • Start Page
  • Show Me
  • Connecting to Excel Files
  • Connecting to Text Files
  • Connect to Microsoft SQL Server
  • Connecting to Microsoft Analysis Services
  • Creating and Removing Hierarchies
  • Bins
  • Joining Tables
  • Data Blending

Module 2: Learn Tableau Basic Reports (Duration: 5Hrs)

  • Parameters
  • Grouping Example 1
  • Grouping Example 2
  • Edit Groups
  • Set
  • Combined Sets
  • Creating a First Report
  • Data Labels
  • Create Folders
  • Sorting Data
  • Add Totals, Sub Totals and Grand Totals to Report

Module 3: Learn Tableau Charts (Duration: 4Hrs)

  • Area Chart
  • Bar Chart
  • Box Plot
  • Bubble Chart
  • Bump Chart
  • Bullet Graph
  • Circle Views
  • Dual Combination Chart
  • Dual Lines Chart
  • Funnel Chart
  • Traditional Funnel Charts
  • Gantt Chart
  • Grouped Bar or Side by Side Bars Chart
  • Heatmap
  • Highlight Table
  • Histogram
  • Cumulative Histogram
  • Line Chart
  • Lollipop Chart
  • Pareto Chart
  • Pie Chart
  • Scatter Plot
  • Stacked Bar Chart
  • Text Label
  • Tree Map
  • Word Cloud
  • Waterfall Chart

Module 4: Learn Tableau Advanced Reports (Duration: 6Hrs)

  • Dual Axis Reports
  • Blended Axis
  • Individual Axis
  • Add Reference Lines
  • Reference Bands
  • Reference Distributions
  • Basic Maps
  • Symbol Map
  • Use Google Maps
  • Mapbox Maps as a Background Map
  • WMS Server Map as a Background Map

Module 5: Learn Tableau Calculations & Filters (Duration: 6Hrs)

  • Calculated Fields
  • Basic Approach to Calculate Rank
  • Advanced Approach to Calculate Rank
  • Calculating Running Total
  • Filters Introduction
  • Quick Filters
  • Filters on Dimensions
  • Conditional Filters
  • Top and Bottom Filters
  • Filters on Measures
  • Context Filters
  • Slicing Fliters
  • Data Source Filters
  • Extract Filters

Module 6: Learn Tableau Dashboards (Duration :4Hrs)

  • Create a Dashboard
  • Format Dashboard Layout
  • Create a Device Preview of a Dashboard
  • Create Filters on Dashboard
  • Dashboard Objects
  • Create a Story

Module 7: Server (Duration: 5Hrs)

  • Tableau online.
  • Overview of Tableau Server.
  • Publishing Tableau objects and scheduling/subscription.

Upcoming Batches

Start Date End Date Time (EST) ( UTC -5) Day
13 Dec 2019 10 Jan 2020 (08:30 PM - 11:00 PM) Fri,Sat
14 Dec 2019 11 Jan 2020 (08:30 AM - 11:00 AM) Sat,Sun
16 Dec 2019 13 Jan 2020 (08:30 PM - 10:00 PM) Mon-Fri
17 Dec 2019 14 Jan 2020 (10:00 AM - 11:30 AM) Tue-Sat
17 Dec 2019 14 Jan 2020 (08:30 AM - 10:00 AM) Tue-Sat

FAQ

Detailed installation of required software will be displayed in your LMS. Our support team will help you to setup software if you need assistance. Hardware requirements need to be fulfilled by participants.

No worries. It might happen. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. And if required you can even attend that topic if any other live batches.

Top-notch professionals in that field who understands how to convey things in technical as well as subject matter experts.

We offer this course in “Live Instructor-Led Online Training” mode. Through this way you won’t mess anything in your real-life schedule. You will be shared with live meeting access while your session starts.

You can get a sample class recording to ensure you are in right place. We ensure you will be getting complete worth of your money by assigning a best instructor in that technology.

We are absolutely loved to talk in-person about group training (or) corporate training. So, please get in touch with our team through “Quick Enquiry”, “Live Chat” or “Request Call-back” channels.

Payments can be made using any of the following options and a receipt of the same will be issued to you automatically via email.

  1. Visa Debit Card / Credit Card
  2. American Express
  3. Master Card, Or
  4. PayPal

You can reach us through +91-9791273737 / +91-9791237373. Or you can share your quires through enq@gangboard.com. Estimated turnaround time will be 24 hours for emails.

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