Data Science

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Best Data Science Online Training Institute In Hyderabad

Supreet Solutions Provides Best Data Science Online Training in Hyderabad by 15+yrs of Real-time Industry Experts. Data Science online training will give you a very clear introduction of Data Science and can make you understand the clear vision of Data Science Management.

We in Supreet Solutions Data Science online training specially designed for the fresher’s to understand in-depth knowledge of Data Science and End eavour their career in  Industry and also we mind the Data Science existing professionals who are trying to upgrade their career into the top level of Management in Data Science.

Data science Live Demo:

Course objectives:-

The course is designed to provide in- depth subject and knowledge of handling business data and Analytics’ tools that can be used for problem solving and decision making using real business case studies.

The outcome of the course, the participants will be able to:

  • Understand the foundations of data science; the role of descriptive, predictive and prescriptive analytics.
  • Understand the emergence of business analytics as a competitive strategy.
  • Analyze data using statistical and data mining techniques
  • Understand relationships between the underlying business processes of an organization.
  • Data visualization
  • Storytelling through data.
  • Decision-making tools
  • Operations Research techniques.
  • Use advanced analytical tools to Analyze complex problems.
  • Manage business processes using analytical and management tools.
  • Analyze and solve customer problems from different industries such as manufacturing, service, retail, software, banking and finance, sports, pharmaceutical, aerospace, etc.
  • Analytics through case studies published by different business schools
  • Understand sources of Big Data and the technologies
  • Algorithms for analyzing big data for inferences.
  • Ability to analyze unstructured data such as social media data and machine generated data.
  • Hands on experience with software such as free software’s Microsoft Excel, Python, R, SAS,SQL,etc and commercial software’s

Benefits from the course

  • Increase revenues to the business
  • Realizing cost efficiency
  • Improving competitiveness
  • Sharing information with a business with presentations
  • Improving the decision-making process
  • Speeding up of decision-making process
  • Responding to business user needs for availability of data on timely basis

1.Introduction to Data Science

  1. What is data science?
    • How is data science different from Bi and Reporting?
  2. Who are data scientists?
    • What skillsets are required?
  3. What do they do?
    • What kind of projects they work on?

2.Business statistics

  1. Data types
    • Continuous variables
    • Ordinal Variables
    • Categorical variables
    • Time Series
    • Miscellaneous
  2. Descriptive statistics
  3. Sampling
    • Need for Sampling?
    • Different types of Sampling
    • Simple random sampling
    • Systematic sampling
    • Stratified Sampling
  4. Data distributions
    • Normal Distribution – Characteristics of a normal distribution
    • Binomial Distribution
  5. Inferential statistics
  6. Hypothesis testing
    • Type I error
    • Type II error
    • Null and alternate hypothesis
    • Reject or acceptance criterion

3.Introduction to R

  • A Primer to R programming
  • What is R? similarities to OOP and SQL
  • Types of objects in R – lists, matrices, arrays, data.frames
  • Creating new variables or updating existing variables
  • IF statements and conditional loops – For, while
  • String manipulations
  • Sub setting data from matrices and frames
  • Casting and melting data to long and wide
  • Merging data sets

4.Exploratory data analysis and visualization

  • Getting data into R – reading from files
  • Cleaning and preparing the data – converting data types (Character to numeric )
  • Handling missing values – Imputation or replacing with place holder values
  • Visualization in R using ggplot2(plots and charts) – Histograms, bar charts, box plot, scatterplots
  • Adding more dimensions to the plots
  • Visualization using Tableau( Introduction)
  • Correlation – Positive , negative and no correlation
  • What is a spurious correlation
  • Correlation vs. causation

5.Introduction to Python:

  • Understanding the reason of Python’s popularity
  • Basics of Python: Operations, loops, functions, dictionaries
  • Advanced operations with text: Finding, Sequencing and basic analytics
    • Ground-up for Deep-Learning

6.Predictive analytics

  1. Different types of predictive analytics – prediction, forecasting, optimization, segmentation
  2. Supervised learning Prediction (Linear)
    • Simple Linear Regression
    • Assumptions
    • Model development and interpretation
    • Sum of least squares
    • Model validation – tests to validate assumptions
    • Multiple linear regression
    • Disadvantages of linear models


  1. Logistic Regression
    1. Need for logistic regression
    2. Logit link function
    3. Maximum likelihood estimation
    4. Model development and interpretation
    5. Confusion Matrix – error measurement
    6. ROC curve
    7. Measuring sensitivity and specificity
    8. Advantages and disadvantages of logistic regression models
  2. Decision trees
    • C5.0
    • Classification and Regression trees(CART)
        • Process of tree building
        • Entropy and Gini Index
        • Problem of over fitting
        • Pruning a tree back
        • Trees for Prediction (Linear) – example
        • Tress for classification models – example
        • Advantages of tree based models?
  1. KNN – K nearest neighbors
    1. Advantages and disadvantages of KNN

3.Re-Sampling and Ensembles Methods

  1. Bagging
  2. Random Forests
  3. Boosting – Gradient boosting machines

4.Advanced methods

  • Support Vector machines
  • Neural networks
  • Introduction to deep learning
  • Introduction to online learning

5.Un-Supervised learning

  • Hierarchical clustering
  • K-Means clustering
  • Distance measures
  • Applications of cluster analysis – Customer Segmentation

6.Time series analysis – Forecasting

  1. Simple moving averages
  2. Exponential smoothing
  3. Time series decomposition
  4. ARIMA Collaborative filtering
  5. User based Filtering
  6. Item based Filtering

7.Model validation and deployment

  • Error measurement
    • RMSE – Root Mean squared error
    • Misclassification rate
    • Area under the curve (AUC)

8.Practical use cases and best practices

  1. Business problem to an analytical problem
  • Problem definition and analytical method selection
    1. Guidelines in model development

9. Introduction to big-data and other tools ( Python and R-Server)

  1. Big data and analytics?
  • Leverage Big data platforms for Data Science
    1. Introduction to evolving tools g Spark
  • Machine learning with Spark

10.Introduction to Azure cloud and Big-Data computing over cloud

  1. Creation of R-Server clusters
  2. Computation of Big-Data ML algorithms over the Azure cloud

11.Introduction to Deep Learning

  1. What is DL and how does it score better over traditional MLs?
  2. Convolutional and Perceptron models
  3. Comparison between DL and ML performances over the MNIST dataset

12.Analytical Visualisation with Tableau

  1. Why is it important for Data-Analyst
  2. Tableau workbook walkthrough
  3. Instruction of creation of your own workbooks
  4. Demo of few more workbooks

13.Offerings from Kelly.

  1. Mock interviews questions and case studies walkthrough over Azure Cortana gallery
  2. Guidance to prepare resumes
  3. Information on companies and industry trends on data science

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