Antrix Academy of Data Science TM

Transcription

1 TM Preparing for MOST Promising Career Opportunities in Data Analytics... Excel Tableau SAS Excel & SQL IBM SPSS Business Analytics

2 COURSES # Duration* 1 Excel Proficiency 5 Hrs 2 Data Analytics with SAS 20 Hrs 3 Data Analytics with IBM SPSS 10 Hrs 4 Working with SQL Concepts 5 Hrs 5 Data Visualization with Tableau 10 Hrs 6 Capstone Projects and Case Studies Classroom Training 50 Hrs/3 Months Weekend Classroom Noida, U.P Project Case Studies Loan Prediction Image Processing Heart Disease Prediction House Price Prediction Segmentation and Profiling Text Mining on Political Speeches Placement Support. Connecting placement opportunities to candidates 2

3 Career Impact Thanks to the digital revolution that is sweeping the world and India in particular, data scientists are now the most sought-after professionals by big corporations as well as startups. And companies across industries are rewarding good data analysts and scientists with desirable career growth and salaries. An estimated 2.7 million job postings for Data Analytics and science are projected in the United States by source With more and more companies understanding the importance of Big Data as a useful source for gaining insights and making informed decision- the demand for Data Analytic specialists who can define the Big Data, uncover hidden pattern, spot opportunities and create insights for the betterment of a business, are surely benefiting from most trending job opportunities in source 3

4 Data Analyst Salary & Opportunities A Data Analyst earns an average salary of Rs 349,284 per year. Most people move on to other jobs if they have more than 10 years' experience in this career. The highest paying skills associated with this job are SAS, Data Modeling, R, Big Data Analytics, and Statistical Analysis. Experience strongly influences income for this job. Common Career Paths for Data Analyst - Source 4

5 Data Science with MS Excel Excel - Basic Introduction to Excel Working with Formulas and functions Formatting & Conditional Formatting Filtering, sorting, paste special etc Functions (Logical & Text, Mathematical, Statistical etc) Data Manipulation & Data Aggregation Data Analysis using functions Excel - Advanced Analyzing Data using Pivots Descriptive Statistics Creating Charts & Graphics Data analytics tool (What -if analysis, Goal seek, Data Table, Solver) Protecting Workbooks, worksheets and formulas Introduction to VBA Working with VBE (Visual Basic Editor) Introduction to Excel Object Model Understanding of Sub and Function Procedures Key Component of Programming Language Understanding of If, Select Case, With End With Statements Looping with VBA User Defined Function Some Commonly Used Macro Examples Error Handling Object and Memory Management in VBA User Form Controls ActiveX Controls Communicating with Database MS Access through ADO - Exporting/Importing Data Creating Dashboard with Excel Working with Slicer Tool for filtering Dashboard and Time Series Understanding the Strategies for creating a Effective Dashboard Working with Pivot Tables & Pivot Charts Working with Report Connection & Pivot Filters 5

6 Data Science with SAS SAS - Introduction - Data importing Introduction to SAS, GUI Concepts of Libraries, PDV, data execution etc Building blocks of SAS (Data & Proc Steps - Statements & options) Debugging SAS Codes Importing different types of data & connecting to data bases Data Understanding(Meta data, variable attributes(format, informat, length, label etc)) SAS Procedures for data import /export / understanding(proc import/proc contents/proc print/proc means/proc feq) SAS - Data Manipulation Data Manipulation steps(sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting, etc) Data manipulation tools (Operators, Functions, Procedures, control structures, Loops, arrays ) SAS Functions (Text, numeric, date, utility functions) SAS Procedures for data manipulation (Proc sort, proc format etc) SAS Options (System Level, procedure level) SAS - Exploratory Data Analysis & Data visualization Introduction exploratory data analysis Descriptive statistics, Frequency Tables and summarization Univariate Analysis (Distribution of data & Graphical Analysis) Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis) SAS Procedures for Data Analysis(proc freq/proc means/proc summary/proc tabulate/proc univariate etc) SAS Procedures for Graphical Analysis (Proc Sgplot, proc gplot etc) SAS - Reporting - Output Exporting Introduction to Reporting SAS Reporting Procedures (Proc print, Proc Report, Proc Tabulate etc) Exporting data sets into different formats (Using proc export) Concept of ODS (output delivery system) ODS System - Exporting output into different formats 6

7 Advanced SAS (Proc SQL - Macros) - Optimizing SAS Introduction to Advanced SAS - Proc SQL & Macros Understanding select statement (From, where, group by, having, order by etc) Proc SQL - Data creation/extraction Proc SQL - Data Manipulation steps Proc SQL - Summarizing Data Proc SQL - Concept of sub queries, indexes etc SAS Macros - Creating/defining macro variables SAS Macros - Defining/calling macros SAS Macros- Concept of local/global variables SAS Macros - Debugging techniques Know How of Statistic Concepts Introduction of Statistics Descriptive and inferential statistics Explanatory Versus Predictive Modeling Population and samples Uses of variable independent and dependent Types of variables quantitative and categorical Descriptive Statistics Introduction Descriptive Statistics Introduction Histogram, Measures of shape skewness, Box Plots Univariante Procedure,Statistical graphics procedures The SGPLOT Procedure, ODS Graphics Output Using SAS to picture your data Confidence Intervals for the Mean Introduction Distribution of sample means Normality and the central limit theorem Calculation of 95% confidence interval Hypothesis Testing introduction Decision Making Process Steps in Hypothesis Testing Types of error and power The p value effect size and sample size Statistical Hypothesis Test the t statistic t distribution and two sided t test 7

8 Data Analytics with IBM SPSS 1. Statistics? a. The Research Process b. Initial Observation c. Generate Theory d. Generate Hypotheses e. Data collection to Test Theory f. What to measure g. How to Measure h. Analyze data i. Descriptive Statistics: Overview j. Central Tendency k. Measure of variation l. Coefficient of Variation m. Fitting Statistical Models n. Conclusion 2. Building statistical models a. Types of statistical models b. Populations and samples c. Simple statistical models d. The mean as a model e. The variance and standard deviation f. Central Limit Theorem g. The standard error h. Confidence Intervals i. Test statistics j. Non-significant results and Significant results: k. One- and two-tailed tests l. Type I and Type II errors m. Effect Sizes n. Statistical power 3. SPSS Environment a. To explore the key windows in SPSS b. Data editor c. The viewer d. The syntax editor e. How to create variables f. Enter Data and adjust the properties of your variables g. How to Load Files and Save h. Opening Excel Files i. Recoding Variables j. Deleting/Inserting a Case or a Column k. Selecting Cases 4. Exploring data with graphs 8

9 a. The SPSS Chart Builder b. Histograms: a good way to spot obvious problems c. Boxplots (box whisker diagrams) d. Graphing means: bar charts and error bars e. Simple bar charts for independent means f. Clustered bar charts for independent means g. Simple bar charts for related means h. Clustered bar charts for related means i. Clustered bar charts for mixed designs j. Line charts k. Graphing relationships: the scatterplot l. Simple scatterplot m. Grouped scatterplot n. Simple and grouped -D scatterplots o. Matrix scatterplot p. Simple dot plot or density plot q. Drop-line graph 5. Correlation a. Standardization and the correlation coefficient b. The significance of the correlation coefficient c. Confidence intervals for r d. Correlation in SPSS i. Bivariate correlation ii. Pearson s correlation coefficient iii. Spearman s correlation coefficient iv. Kendall s tau (non-parametric) v. Biserial and point biserial correlations vi. Partial correlation vii. The theory behind part and partial correlation viii. Partial correlation using SPSS ix. Semi-partial (or part) correlations e. Comparing correlations f. Comparing independent rs g. dependent rs h. Calculating the effect size i. How to report correlation coefficients 6. Regression a. An introduction to regression b. Some important information about straight lines c. The method of least squares d. Assessing the goodness of fit: sums of squares, R and R2 e. Doing simple regression on SPSS f. Multiple regression: the basics g. How to do multiple regression using SPSS h. Descriptive i. Checking assumptions 9

10 SQL Concepts for Data Visualization 1) Introduction to Databases a) Terminologies - Records, Fields, Tables b) Introduction to database normalisation c) Primary Key d) How data is accessed 2) Introduction to SQL a) SQL Syntax b) SQL data Types c) SQL Operators d) Table creation in SQL : Create, Insert, Drop, delete and updating 3) Introduction to SQL - Table access & Manipulation a) Select with Where Clause (In between, logical operators, wild cards, order, group by) b) SQL constraints c) Concepts of Join - Inner, Outer 4) Case study 10

11 Tableau for Data Visualization 1. Introduction to Tableau Desktop a. Overview of Business Intelligence b. Introduction to Tableau Desktop c. Use and benefits of Tableau Desktop d. Tableau's Offerings 2. Tableau Desktop Interface a. Data Source Page b. Worksheet Interface c. Creating a Basic View 3. Connecting Data Sources a. Data Types b. Data Roles c. Visual Cues for Fields d. Data Preparation e. Data Source optimization 4. Organizing Data a. Filtering Data b. Sorting Data c. Creating Combined Fields d. Creating Groups and Defining Aliases e. Working with Sets and Combined Sets f. Drilling and Hierarchy g. Adding Grand Totals and Subtotals 5. Formatting Data a. Effectively use Titles, Captions, and Tooltips b. Format Results with the Edit Axes c. Formatting your View d. Formatting results with Labels and Annotations e. Enabling Legends per Measure f. Calculations g. Use Strings, Date, Logical, and Arithmetic Calculations h. Create Table Calculations i. Discover Ad-hoc Analytics j. Perform LOD Calculations 6. Visualizations a. Creating Basic Charts such as Heat Map, Tree Map, Bullet Chart, and so on b. Creating Advanced Chart as Waterfall, Pareto, Gantt, Market Basket 7. Create Dashboards and Stories a. Dashboard Interface b. Build Interactive Dashboards c. Explore Dashboard Actions d. Best Practices for Creating Effective Dashboards e. Story Interface f. Creating Stories 11

12 Capstone Projects and Case Studies 1) At the end of this Capstone Project, you'll be able to make sense of the given data and gain insights on how to use Analytical techniques effectively to address the business challenge. 2) Once you've completed the project, you'll be better able to apply analytical techniques on a business case and accordingly prepare a detailed report. 3) In case you don't have any relevant experience in Analytics, this project will enable you to showcase your expertise in a job interview. Different Case Studies & Projects 1) Loan Prediction- Helping a credit card company in automating the loan approval request i.e. whether an applicant should be granted loan or not by training machine on company s historical data. 2) Image Processing a) Using OCR Technique to extract text data from Images. b) Digit recognition -Build machine learning predictive model to predict the actual digit from that digit s image. 3) Heart Disease Prediction Using ML - Help a healthcare expert to predict the propensity of a patient to have heart disease. 4) House Price Prediction 5) Segmentation and Profiling - Using U.S. Crime dataset 6) Text Mining on Political Speeches- Predicting the orator of a speech 12