Data Science training in kukatpally





Introduction to Data Science

Kosmik Technologies is one of the institutes for Data Science training in Hyderabad. The main tools and ideas required for Data Scientist or Business Analyst or Data Analyst. The course overview of the data, questions, tools. That analysts and data scientists Work with real time experts. There are two components to this course, first one is the conceptual introduction. The ideas behind turning data into actionable knowledge. The second is a practical introduction. The tools used in the program like R Programming with domain-specific use cases. Kosmik Technologies is providing the  Data Science Training in Hyderabad.

Basic Concepts of Statistics:
1. Descriptive Statistics and Probability Distributions:
• Introduction to Statistics
• Different Types of Variables
• Measures of Central Tendency with examples           

  •  Mean
  • Mode
  • Median

• Measures of Dispersion

  • Range
  • Variance
  • Standard Deviation

• Probability & Distributions
• Probability Basics
• Binomial Distribution and its properties
• Poisson distribution and its properties
• Normal distribution and its properties

2. Inferential Statistics and Testing of Hypothesis
• Sample methods

  • Sampling and types of sampling
  • Definitions of Sample and Population
  • Importance of sampling in real time
  • Different methods of sampling
  • Simple Random Sampling with replacement and without replacement
  • Stratified Random Sampling

• Different methods of estimation
• Testing of Hypothesis & Tests

  • Null Hypothesis and Alternate Hypothesis
  • Level of Significance and P value
  • t-test and its properties
  • Chi-square test and its properties
  • Z test

• Analysis of Variance

  • F-test
  • One and Two way ANOVA

3. Covariance & Correlation

  •  Importance and Properties of Correlation
  • Types of Correlation with examples

Predictive Modeling Steps and Method with the Live example:
• Data Preparation

  • Variable Selection
  • Transformation of the variables
  • Normalization of the variables

• Exploratory Data analysis

  • Summary Statistics
  • Understanding the patterns of the data at single and many dimensions
  • Missing data treatment using different methods
  • Outlier’s identification and treating outliers
  • Visualization of the data use Dimensional Types
  • Bar chart, Histogram, Box plot, Scatter plot, Bubble chart, Word cloud etc…

• Model Development

  • Selection of the sample data
  • selecting the appropriate model based on the rule and data availability

• Model Validation

  • Model Implementation
  • Key Statistical parameters checking
  • validating the model results with the actual result

• Model Implementation

  • implementing the model for future prediction

• Real time telecom business use case with detail explanation
• Introducing a couple of real time use cases.

   Supervised Techniques:
• Many linear Regressions

  • Linear Regression – Introduction – Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)
  • Validation of Linear Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc)
  • Interpretation of Results – Business Validation – Implementation on new data
  • Real time case Manufacturing and Telecom Industry revenue using the models

• Logistic Regression

  • Logistic Regression – Introduction – Applications
  • Linear Regression vs. Logistic Regression vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Standard model metrics (Concordance, Variable significance, Hosmer Lemeshow Test, Gini, KS, Misclassification etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve)
  • Probability Cut-offs, Lift charts, Model equation, drivers etc)
  • Interpretation of Results – Business Validation – Implementation on new data
  • Real time case study to predict the Churn customers in the Banking and Retail industry

• Partial Least Square Regression

  •  Partial Least Square Regression – Introduction – Applications
  • Difference between Linear Regression and Partial Least Square Regression
  • Building PLS Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)
  • Interpretation of Results – Business Validation – Implementation on new data
  • sharing the real time example to identify the key factors which are driving the Revenue

Variable Reduction Techniques
• Factor Analysis
• Principle component analysis

  • Assumptions of PCA
  • Working Mechanism of PCA
  • Types of Rotations
  • Standardization
  • Positives and Negatives of PCA

Supervised Techniques Classification:
• Difference between CHAID and CART
• Random Forest

  • Decision tree vs. Random Forest
  • Data Preparation
  • Missing data imputation
  • Outlier detection
  • handling imbalance data
  • Random Record selection
  • Random Forest R parameters
  • Random Variable selection
  • Optimal number of variables selection
  • Calculating out Of Bag (OOB) error rate
  • Calculating Out of Bag Predictions

• A couple of Real time uses cases which related to Telecom and Retail Industry. Identify of the Churn.

Unsupervised Techniques:
• Segmentation for Marketing Analysis

  • Need for segmentation
  • Criterion of segmentation
  • Types of distances
  • Clustering algorithms
  • Hierarchical clustering
  • K-means clustering
  • Deciding number of clusters
  • Case study

• Business Rules Criteria
• Real time use case to identify the Most Valuable revenue generating Customers.

Time series Analysis:
• Forecasting – Introduction – Applications
• Time Series Components (Trend, Seasonality, Cyclicity, and Level) and Decomposition
• Basic Techniques –

  • Averages,
  • Smoothening

• Advanced Techniques

  • AR Models,
  • UCM
  • Hybrid Model

• Understanding Forecasting Accuracy – MAPE, MAD, MSE etc
• Couple of use cases, to forecast the future sales of products

Text Analytics:
• Gathering text data from the web and other sources
• Processing raw web data
• Collecting Twitter data with Twitter API
• Naive Bayes Algorithm

  • Assumptions and of Naïve Bayes
  • Processing of Text data
  • Handling Standard and Text data
  • Building Naïve Bayes Model
  • Understanding standard model metrics
  • Validation of the Models (Re running Vs. Scoring)

• Sentiment analysis

  • Goal Setting
  • Text Preprocessing
  • Parsing the content
  • Text refinement
  • Analysis and Scoring

• Use case of Health care industry, identify the extracting the data from the TWITTER.

Visualization Using Tableau:
• Live connectivity from R to Tableau
• Generating the Reports and Charts

Data Science Training In Kukatpally

Kosmik Technologies is the one Data Science training in Kukatpally. That offers good course support for the candidates throughout the course. The demand for big data analytics is future demands of the information technology. There is scope for every IT enthusiast to look into this growing field.  That programming, the field is all the purpose of saving and troubleshooting the data. Kosmik technologies are providing Data Science Courses in Hyderabad by all the modules. 


SESSION 1: Getting Started with R
• What is statistical programming?
• The R package
• Installation of R
• The R command line
• Function calls, symbols, and assignment
• Packages
• Getting help on R
• Basic features of R
• Calculating with R

SESSION 2: Matrices, Array, Lists, and Data Frames
• Character vectors

• Operations on the logical vectors

• Creating the matrices and operations on it

• Creating the array and operations on it

• Creating the lists and operations on it

• Making data frames

• Working with data frames

SESSION3: Getting Data in and out of R
• Importing Data into R
• Exporting Data in R
• Copy Data from Excel to R
• Loading and Saving Data with R
• Importing different types of file formats

SESSION4: Data Manipulation and Exploration:
• Variable transformations
• Creating Dummy variables
• Data set options (Rename, Label)
• Keep / Drop Columns
• Identification and Dealing with the Missing data
• Sorting the data
• Handling the Duplicates
• Joining and Merging (Inner, Left, Right and Cross Join)
• Calculating Descriptive Statistics
• Summarize numeric variables
• Summarize factor variables
• Transpose Data
• Aggregated functions using Group by
• Dplyr and data table packages for the data manipulation
• Data preparation using the sqldf package

SESSION5: Conditional Statements and Loops:
• If Else
• Nested If Else
• For Loop
• While Loop

SESSION6: Functions:
• Character Functions
• Numeric Functions
• Apply Function on Rows
• Converting a factor to integer
• Indexing Operators in List

SESSION7: Graphical procedures
• Pie chart
• Bar Chart
• Box plot
• Scatter plot
• Multi Scatter plot
• Word cloud etc.…

 SESSION8: Advanced R and Real time analytics examples:
• Data extraction from the Twitter
• Text Data handling
• Positive and Negative word cloud
• Required packages for the analytics
• Sentiment analysis using the real time example
• R code automation
• Time series analysis with the real time Telecom data
• Couple of examples with the time series data

What are the pre-requisites for this Course…..?

Kosmik Technologies provide Data Science training in Kukatpally that offer courses in Data science, but this institute gives the best out of all existing institutes. This present data science course, most of the Information technology. The professionals having analytical thinking are needed. The basic trending and analyzing till the typical analytical thinking about the data. People having the more technical background and quantitative skills. They can attend the Course for improving technical knowledge to move further.
We provide classroom, corporate training and online Data science courses in Hyderabad by real time experts.