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QSHORE TECHNOLOGIES Reach us at 9030821111 Email: Infoqshore.com DATA SCIENCE About the Course: In this course you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst/Analytics Manager/Actuarial Scientist/Business Analytic Practitioners. The course gives an overview of the data questions and tools that data analysts and data scientists work with. The course is a combination of various data science concepts such as machine learning visualization data mining programming data munging etc. There are three components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is manual calculations will be shown on how formulae’s are used behind the logics. The third is a practical introduction to the tools that will be used in the program like R Programming and EXCEL. Course features: ✓ Exclusive doubt clarification session on every weekend ✓ Real Time Case Study driven approach ✓ Placement Assistance Pre-Requisite / Qualification: ✓ Any Graduate. No programming and statistics knowledge or skills required Duration of the course: ✓ 90 Hours On working days-one hour and weekends-3hrs. Mode of course delivery: ✓ Classroom/Online Training INTRODUCTION • What is Data Science – Introduction. • What background is required • Why Data Science • Importance of Data Science. • Demand for Data Science Professional. • Brief Introduction to Big data and Data Analytics. • Lifecycle of data science. • Tools and Technologies used in data Science. • What is Machine Learning • Different types of Data Science Tasks.

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QSHORE TECHNOLOGIES Reach us at 9030821111 Email: Infoqshore.com BUSINESS STATISTICS • Descriptive statistics and Inferential Statistics • Sample and Population • Variables and Data types • Percentiles • Measures of Central Tendency • Measures of Spread • Skeweness Kurtosis • Degrees of freedom • Variance Covariance Correlation • Standardization/Scaling • Probability • Expected of ‘x’ • Sampling Distribution • Standard Probability Distribution Functions • Bernoulli Binomial Normal distributions • Standard Normal Deviate • Decision Making Rules • Test of Hypothesis • One sample t-Test Chi-square • Two sample t-Test Analysis of Variance ANOVA EXPLORATORY DATA ANALYSIS AND VISUALIZATION • Summary Statistics • Data Transformations • Outlier Detection and Management • Charts and Graphs • One Dimensional Chart • Box plots • Bar graph • Histogram • Scatter plots • Multi-Dimensional Charts • Fancy Charts - Bubble charts DATA PRE-PROCESSING • Data Types and Conversions • Binning Scaling Standardization Normalization • Min-max Scaling • Missing values Treatment • Imputation

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QSHORE TECHNOLOGIES Reach us at 9030821111 Email: Infoqshore.com PREDICTION ANALYTICS ➢ Simple Linear Regression ➢ Multiple Linear Regression ➢ Estimation of Model Parameters ➢ Hypothesis Testing in Multiple Linear Regression ➢ Extra sum of squares ➢ R – Square R- Square Adjusted ➢ Variable Selection a. All Possible Regressions b. Sequential Selection Forward Backward Stepwise ➢ Multicollinearity – VIF ➢ Residual Analysis/Regression Diagnostics. ➢ Polynomial Regression ➢ Transformations a. Bulging Rules b. Box Tidwell c. Box cox d. Weighted Least Square ➢ Dummy variables a. General Concepts of Indicator variables. ➢ Predicted Error sum of squares PRESS ➢ Assessing Performance a. Variance Biased Trade-off b. Resampling Methods c. Cross Validation d. Leave one out Cross validation e. k-Fold Cross Validation f. Bootstrap ➢ Logistic Regression A Case Study will be presented on Logistic Regression

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QSHORE TECHNOLOGIES Reach us at 9030821111 Email: Infoqshore.com MACHINE LEARNING Introduction to Supervised and unsupervised Learning ➢ Neural Networks a. Network Topology b. Single Layer Perceptron c. Multi-Layer perceptron d. Feed forward and Back propagation Models ➢ Introduction to Deep Learning ➢ Association Rules a. Market Basket Analysis b. APRIORI c. Support Lift Confidence ➢ Nearest-Neighbour Methods KNN – Classifier a. Euclidian Distance b. Hamming Distance ➢ Decision Tree a. Finding Root Node Intermediate Nodes Terminal Nodes b. Construction of Rules c. Miss classification d. Gini Index e. Overfitting and Prunning f. Regression Trees ➢ Boosting Bagging and Random Forest a. Resampling Methods b. Resampling methods with Replacement c. Resampling methods without Replacement d. Random Forest ➢ Dimensional Reduction Techniques 1. Principle Component Analysis a. Eigen values and Eigen Vectors 2. Cluster Analysis a. Hierarchal Clustering b. Linkage Methods c. Non- Hierarchal Clustering d. K-Means Clustering

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QSHORE TECHNOLOGIES Reach us at 9030821111 Email: Infoqshore.com ➢ Text Mining / Natural Language processing a. Unstructured Data b. Text Analytics c. Cleaning Text data d. Tokenization e. Pre-processing f. Word counts and word clouds g. Sentiment Analysis h. Text classification i. Distance measures ➢ Introduction to probabilistic methods Introduction a. Naive Bayes b. Joint and Condition probabilities c. Classification using Naive Bayes Approach ➢ Support Vector Machines a. Maximum Margin Classifier b. Support vector Classifier c. Support vector machines d. Kernels – Linear and Non Linear ➢ PYTHON - PROGRAMMING • How to install python Anaconda • How to install sciKit Learn Anaconda • How to work with Jupyter Notebook • How to work with Spyder IDE • Strings • Lists • Tuples • Sets • Dictionaries • Control Flows • Functions • Formal/Positional/Keyword arguments • Predefined functions range len enumerates etc… • Data Frames • Packages required for data Science in Python • Lab/Coding

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QSHORE TECHNOLOGIES Reach us at 9030821111 Email: Infoqshore.com ➢ Introduction to NumPy • One-dimensional Array • Two-dimensional Array • Pr-defined functions arrange reshape zeros ones empty • Basic Matrix operations • Scalar addition subtraction multiplication division • Matrix addition subtraction multiplication division and transpose • Slicing • Indexing • Looping • Shape Manipulation • Stacking ➢ Introduction to Pandas • Series • DataFrame • df.GroupBy • df.crosstab • df.apply • df.map ➢ Apache Spark Analytics What is Spark Introduction to Spark RDD Introduction to Spark SQL and Dataframes Using R-Spark for machine learning Hands-on: installation and configuration of Spark Hands on Spark RDD programming Hands on of Spark SQL Dataframe programming Using R-Spark for machine learning programming

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QSHORE TECHNOLOGIES Reach us at 9030821111 Email: Infoqshore.com ➢ R – PROGRAMMING 1. Getting R 1.1 Downloading R 1.2 R Version 1.3 32-bit versus 64-bit 1.4 Installing 2. The R Environment 2.1 Command Line Interface 2.2 RStudio 3. R Packages 3.1 Installing Packages 3.2 Loading Packages 4. Reading Data into R 4.1 Reading CSVs 4.2 Excel Data 4.3 Clipboard 5. Advanced Data Structures 5.1 Data.frames 5.2 Lists 5.3 Matrices 5.4 Arrays 5.5. Factors 6. Basics of R 6.1 Basic Math 6.2 Variables 6.3 Data Types 6.4 Vectors 6.5 Calling Functions 6.6 Function Documentation 6.7 Missing Data 7. Control Statements 7.1 if and else 7.2 switch 7.3 ifelse 8. Loops 8.1 for Loops

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QSHORE TECHNOLOGIES Reach us at 9030821111 Email: Infoqshore.com 8.2 while Loops 8.3 Controlling Loops 9. Group Manipulation 9.1 Apply Family 9.2 aggregate 10. Data Reshaping 10.1 cbind and rbind 10.2 Joins 10.3 Reshape2 11. String Theory 11.1 paste 11.2 sprintf 11.3 Extracting Text/ Regular Expressions 12. Graphs with R and GGPlot2 12.1 Basic and Interactive Plots 12.2 Dendrograms 12.3 Pie Chart and Its Alternatives 12.4 Adding the Third Dimension 12.5 Visualizing Continuous Data 13. Basic Statistics 13.1 Summary Statistics 13.2 Correlation and Covariance 13.3 T-Tests 13.4 ANOVA 14. Probability Distributions 14.1 Normal Distribution 14.2 Binomial Distribution Course Highlights ✓ A Dedicated Portal For Practicing. ✓ Real Time Project Data Models to Work ✓ 1-1 Mentorship ✓ Internship Offers for Freshers. ✓ Weekly Assignments. ✓ Weekly Doubt Sessions\ ✓ Resume Preparation Tips ✓ Interview Guidance And Support. ✓ Dedicated HR Team for Job Support And Placement Assistance.

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