ACHIEVEMENTS FROM TRAINING

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Bertina Jennings
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1 LEARN WELL TECHNOCRAFT DATA SCIENCE/ MACHINE LEARNING SYLLABUS 8TH YEAR OF ACCOMPLISHMENTS AUTHORIZED GLOBAL CERTIFICATION CENTER FOR MICROSOFT, ORACLE, IBM, AWS AND MANY MORE / , SUPREME CENTER, ITI ROAD, ABOVE PIZZA HUT, AUNDH, PUNE

2 ACHIEVEMENTS FROM TRAINING C A N D I D A T E W I L L B E A B L E T O S H O W 2-3 Y E A R S E X P E R I E N C E A F T E R T R A I N I N G. R E A L T I M E S C E N A R I O S, C A S E S T U D I E S, P R O J E C T S I N C L U D E D. R E A L T I M E D A T A P R O V I D E D F O R P R A C T I C E. S O F T W A R E W I L L B E I N S T A L L E D O N C A N D I D A T E S M A C H I N E. I N D I V I D U A L 1 O N 1 D I S C U S S I O N S F O R R E S U M E M O D I F I C A T I O N S. L E A R N F R O M I N D U S T R Y E X P E R T S. G L O B A L C E R T I F I C A T I O N P R E P A R A T I O N. A P P E A R F O R G L O B A L C E R T I F I C A T I O N A T L E A R N W E L L T E C H N O C R A F T I T S E L F. G E T D I S C O U N T E D C E R T I F I C A T I O N V O U C H E R S. A U T H O R I Z E D G L O B A L C E R T I F I C A T I O N C E N T E R F O R P E A R S O N, P S I, K R Y T E R I O N.

3 Data Scientist Syllabus Data Scientist/Analyst, now a day s the most buzzing work in IT world. Businesses are generating so much of the data and the need to analyze the data is top most priority. Keeping in line with the market requirements, as per the job description for data scientist role, we have designed a new course: Complete R Programming: R is a Data Analytical Language SAS: For each module of R, we will cover SAS also(optional) Python: Python is an Data Analytical Language (Optional) Hadoop: Basic to intermediate aspects of Hadoop Spark: Hadoop combined with Spark makes a great combination.(optional) Tableau: It s the Visualization tool, which helps in presenting the reports and graph s to business. Excel/SQL: It s very vital for a Data Scientist to work on excel files and Databases.(Optional) What is R? Birth and Rise of R R Programming Links for the necessary software GUI of R: IDE and Statistical Analysis Interfaces R Workspace GUI of RStudio Basic Operations in R Expressions: Basic Idea Constant Values: Numeric & Non-numeric Arithmetic: Operations and BODMAS Conditions: Equality, Greater Than, Less Than, etc. Function Calls: Introduction to R Functions Symbols & Assignment Keywords: NA, Inf, NaN, NULL, TRUE, FALSE Naming a Variable: Generally accepted conventions Data Types & Data Structures in R Basic data types Basic data structures: Vector, Factor, Matrices, Data Frame, List

4 Subsetting in R Vector Subsetting c() function: Creation of Vectors Using rep() and seq() functions Using factor() to covert vectors to factors Using data.frame() to create data frames Meta data access: dimnames(), rownames(), colnames() Using matrix() to create matrices Using array() to create arrays Subsetting data frames: row subset, column subset, using subset() function Assigning to a subset Basic Operations in R Expressions: Basic Idea Constant Values: Numeric & Non-numeric Arithmetic: Operations and BODMAS Conditions: Equality, Greater Than, Less Than, etc. Function Calls: Introduction to R Functions Symbols & Assignment Keywords: NA, Inf, NaN, NULL, TRUE, FALSE Naming a Variable: Generally accepted conventions Using is.na() to detect NA Subsetting factors Additional Topics on Data structures The recycling rule: Uneven arithmetic operation on vectors Type coercion: Character to Numeric Automatic Type coercion Coercing factors: Using as.factor() function Changing factor levels Attributes: attribute() functions attr()functions names() functions Classes: Idea of OOP in R Dates: As a special class Formulas: As a special class Exploring Objects: summary(), str(), dim() functions

5 Data Import & Export Text formats: Reading Delimited Files read.table() function Using read.fwf() function for fixed width files Using readlines() for reading lines Using write. csv() function to store data as CSV files Reading Excel file: Package XLConnect Reading SPSS file: Package Foreign Reading SAS data file: Package sas7bdat Database connection: The ideas of ODBC connecting in Windows RODBC package: Create and Query database from R Basic SQL Control Structures & User defined Functions Conditional Statements Charting with R If statement: The Structure If Else statement: The Structure ifelse() function Iteration & Looping The for loop The while loop The repeat statement lapply() function sapply() function apply() function User defined function Variable scooping: Global and Local Variables Using user defined functions inside function definition The plot function plot.new() function: Generating new plot object plot.window() function: Creating window points() function: Plotting points axis() function: Generating Axis box() function: Creating enclosure title() function: Assigning title par() function: Fixing plotting parameters lines() function: Adding connector lines Multi figure layout: Creating multiple charts in the same window hist() function: Plotting histograms

6 Kernel Density Plot: The non-parametric probability distribution Comparing Groups via Kernel Density: Comparing two different probability distributions Simple Bar Plot: Visualizing categorical data Staked Bar Plot: Understating category composition Grouped Bar Plot Line Charts Pie Charts Boxplots: Understanding data distributions and outliers Geo Charts Motion Charts Summary statistics for data t tests: Comparing means Anova: Comparing means and causal relations Factor Analysis: Dimension Reduction technique Cluster Analysis: Segmentation and Homogeneous groups of data Analytics & Data Mining Using R Linear Regression: Predicting from uni-linear causality Logistic Regression: Predicting the probability in a binary outcome Situations. Time series Analysis: Automated ARIMA Decision Trees: Conditional inference trees for classification and Profiling Analytics: Association Rule Mining Using R (Market Basket Analysis) Introduction to Association learning Different types of association algorithms Apriori Algorithm: Support, Confidence and Lift Market basket Analysis Text Mining Using R Introduction to Text Mining Keyword search Word cloud Sentiment Analysis Twitter Data Analysis Case Study.

7 SAS Note: For each module for R programming SAS topics will be covered including Regression, Machine Learning and Sentiment Analysis etc. Hadoop Hadoop 1.0 overview and enhancements in Hadoop 2.0 Hadoop installation and setup using Virtual Box and Hortonworks distro. Typical Cluster architecture in Hadoop 1.0 vs Hadoop 2.0 (optional) HDFS architecture MR architecture Java MR example (optional for interested students) Scoop hands on with example Pig hands on with example Hive hands on with example Note: We have a separate 5-6 weekends detailed course for Hadoop also. Introduction to Spark Spark Installation & Overview. Spark SQL Reading data from text files Basics of Spark and core concepts like RDD, caching etc. Understand few famous programs like word count and additionally trying few more. Trying out various APIs offered by Spark Core libraries. Overview of SparkSQL Using Hive meta data with Spark SQL SchemaRDDs Using various File formats like Parquet and JSON Using Spark SQL and Hive UDFs Spark ML Overview of Spark ML Understanding Vectors Understanding Linear regression and running with Spark ML Understanding Logistic regression and running with Spark ML Running Clustering example with Spark ML Dimensionality reduction in Spark using Principal Component Analysis

8 Tableau Overview: User interface basics Connecting to data Dimensions vs. measures Show Me Marks card Simple formatting Building views Building a dashboard Connecting to Excel, CSV and Text Files: Connecting to single or multiple tables Connecting live versus importing the data Editing data connections after initial connection Data source filtering Working with Data: Flat Files (Excel, CSV, Access DB) Relational Databases ODBC Drivers Live or Import Data Connection Metadata Management Multiple Data Connections Creating and Refreshing an Extract Analysis: Hierarchies Sorting Grouping Filtering Aggregations Trend lines Page shelf Forecasting

9 Formatting: Row-banding Analysis: Calculation: Number formatting Text formatting Shading Labels Tooltips Aggregate Calculations Row-Level Calculations Quick Table Calculations Dashboard: Dashboard Objects Filter Actions URL Actions Sizing Tiled and Floating Sheets Dynamic Sheet Titles Tableau Server(Provided availability of License): Publishing the Workbook Scheduling Refresh Extract Managing Authentication and Authorization Monitoring Background Tasks Automation of Reports Note: We have a separate detailed 5-6 weekends course for Tableau also.

10 Also Available Internships - Paid / Free Internship certifications on successful completion Final year Collage Projects on Latest Skills Special Project batches Collage Seminars

Specialist ICT Learning

Specialist ICT Learning APPLIED DATA SCIENCE AND BIG DATA ANALYTICS GTBD7 Course Description This intensive training course provides theoretical and technical aspects of Data Science and Business Analytics.