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Data Science

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Data Science starts from EDA (estimated data analysis) and covers all the techniques for analytics keeping in mind weather it will be logistic or Linear.

  • GOLD
28 Sessions
84 Hours

Skills you will master

Base SAS
Linear regression
Logistic Regression
Random Forest


04:00 PM - 07:00 PM
01:00 PM - 04:00 PM
10:00 AM - 01:00 PM
10:00 AM - 01:00 PM
04:00 PM - 07:00 PM
04:00 PM - 07:00 PM
Mob : +91-9810600764
Address : M8, Lower ground Floor, Sector 14 OLD DLF Gurgaon 122001
Email : info@palin.co.in


About The Program
The data science is designed by Data Scientists keeping in mind of upcoming industry requirement and latest trend to fulfill the pool of manpower. It enhance your technical, conceptual as well as the logical approach towards data, which helps you to understand the data as well as to get insights of it. This program gives you the high quality content to analyze after collection of data from different sources like sensors, web, mobiles and social media using various statistical and machine learning algorithms. Data science training is a Flight to a better career path


During this training participants would be able to use data mining, cleaning n preparation techniques using excel and then slowly switch over the most demanding platforms R and SAS will be able to use various data analytics concepts quantitative and qualitative research consist in respect to data collection, data sample, data analysis…


During this training participants

During this training participants

After the completion of the course you will be able to crack the interviews with most of the renowned organizations like Accenture, IBM, Dunnhumby, EXL, Bank of America, American Express, Fractal Analytics, Cognizant and many more. It’s a 100 hours instructor Led training and unlimited access of training material, recordings, assignments, projects.
Career Advisor
Vinit Kumar, Consultant
Palin Delivers real world relevance with activities and assignments that helps students build critical thinking and analytic skills that will transfer to other courses and professional life.
Program Description
Data science master program is made keeping in mind the profile that most of the corporate are asking for a data scientist. It includes advance excel for data mining purpose and sql for communication with database. SAS and R are platforms to use for analytics using modeling techniques which are used for cluster analysis, time series analysis, market basket analysis and regression. Machine learning algorithm are also added to the course keeping in mind the requirement of industry to include artificial intelligence into analytics. For data visualization and reporting we use tableau. To work with different domains like finance, marketing, ecommerce, banking, insurance, aviation and even games also.
Program Preview

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   Session 1 - Introduction of Data Science

Data Science Discussion, Profile Discussion, Domain Discussion, Tools Discussion

   Session 2 - Introduction to SAS

Introduction to SAS Programming, SAS Environment, Working with the windows (program editor windows, log windows, output windows, results windows, explorer windows), Overview of libraries(Datasets, Data view, Catalog, Referencing Files in SAS Libraries, Basic concepts, Creating a SAS Programs, Components of SAS Programs, Characteristics of SAS Programs , Layout of SAS Programs) and Basic Concepts.

   Session 3 - Understanding Data Step Processing

Program data vector (PDV), Compilation Phase, Execution Phase, Creating a File shortcut with the File Shortcut Assignment Window, Making a file shortcut to a program, Deleting a file shortcut, Browsing and submitting a file shortcut for a SAS program, Viewing file shortcut properties, Importing (Methods for getting data in SAS, Input Style, Assigning variable attributes, Pointers, Informats, Formats).

   Session 4 - Functions & Conditional Statements

Arithmetic Functions, Reading Raw Data from External File, Infile statement, Import, Export, Options, Global options, Local Options, Statements, Global Statements, Local Statements

   Session 5 - Functions & Statements Continuous, Loops & Joins

Date & Time Functions, String Functions, Control Statements, If statement and if else statement, If then else statement, Where statement, Loops (do, dountil, dowhile), Arrays Statements, Retrieving Data from Multiple tables, Natural Join, Inner Join, Outer Join (Right, Left, Full)

   Session 6 - Procedures and Merging

Proc Print, Proc Transpose, Proc Contents, Proc Formats, Proc Append, Proc Tabulate, Proc Import, Proc Report, Proc Export, Proc Datasets, Proc Freq, Proc Means, Proc Reg, Proc ANOVA, Concatenating, Merging, One-to-one merging, One-to-many merging, Many-to-one merging, Many-to-many merging, Matching merging, Updating, Retrieving Data From Multiple Table ( Natural Merge, Inner Merge, Outer Merge)

   Session 7 - Introduction to R

Fundamental of R, Fundamentals of R, Installation of R & R Studio, Getting started with R , Basic & advanced, data types in R , Variable operators in R, Working with R data frames, Reading and writing data files to R, R functions and, loops, Special utility functions, Merging and sorting data.

   Session 8 - Visualisation in R

Case study on data management using R, Data visualization in R, Need for data visualization, Components of data visualization, Utility and limitations, Introduction to grammar of graphics.

   Session 9 - Data Preparation

Data preparation and cleaning using R, Needs & methods of data preparation, Handling missing values, Outlier treatment, Transforming variables, Derived variables, Binning data, Modifying data with Base R, Data processing with dplyr package, Using SQL in R, Understanding the data using Univariate statistics in R, Summarizing data, measures of central tendency, Measures of variability, distributions, Using R to summarize data, Practice assignment.

   Session 10 - Decision Making

Hypothesis testing and ANOVA in R to guide decision making, Introducing statistical inference, Estimators and confidence intervals, Central Limit theorem, Parametric and non-parametric statistical tests, Analysis of variance (ANOVA), Conducting statistical tests, Practice assignment.

   Session 11 - Fundamental of Statistics & Modeling Techniques

Introduction to Predictive Analytics, Predictive modeling in SAS & R, Relevance in industry and need of the hour, Types of analytics – Marketing, Risk, Operations, etc, Future of analytics and critical requirement.

   Session 12 - Basic Analytics

Data Sampling, Hypothesis Testing, Sampling Distribution, Inference, P- Value, Critical Region, T-Test, Chi Square Test, F- Test, Data Preparation, Different Data Type, Data Cleaning, Derived Variables, Aggregate Data.

   Session 13 - Correlation Linear Regression

The Correlation Techniques, Pearson's Correlation Coefficient (r), Variance, Co Variance, Correlation matrix, types of correlation, Simple Linear Regression, Regression Formulas, Assumptions of Classical Linear Regression Model, Zero Covariance and No Multicollinearity, Parameter Estimation Method, Deciding important variables, Model Selection Criterion.

   Session 14 - Multiple Linear Regression

Multicollinearity, Consequences of Multicollinearity, Detection of Multicollinearity, Heteroscedasticity, Autocorrelation, Regression Statistics, ANOVA, Hypotheses Tests for Regression Coefficients, Diagnostic Tests For Regressions, Model Selection, Model Evaluation.

   Session 15 - Logistic Regression

Generalized Linear Models (GLMs), Binary Logistic Regression, SAS Logistic Regression Output Explanation, Fitting the Model in SAS, Fitting the Model in R, Test for Regression Coefficients, Validation Statistics, Goodness of Fit for Logistic Regression(Chi-square Test), Concordance, Prediction Using Logistic Regression, Making Predictions, Multicollinearity in Logistic Regression.

   Session 16 - Clustering & Customer Segmentation

Inter variable dependence, Hierarchical Clustering, k means clustering, Principal Component Analysis(PCA), Data Reduction, Eigen values, Eigen vectors, Assumption of PCA, Interpretation of results

   Session 17 - Predictive Modeling & Forecasting Techniques

Time Series Analysis, Assumptions of Time Series Analysis and Identifying Patterns in Time Series Data, General aspects of analysis, Autoregressive and moving averages, ARIMA Procedures and Time Series Model.

   Session 18 - Introduction to Machine Learning

Machine Learning overview, Nonlinear Least Square, Decision trees: What are decision trees, Entropy and Gini impurity index, Decision tree algorithms, Machine Learning Algorithms Random forest.

   Session 19 - Tableau Introduction

Business Overview, Introduction to Data Visualisation, Introduction to Business Intelligence, Data Visualization tools, Data Connection, Tableau Server & Tableau Reader, Tableau Features

   Session 20 - Reports & Dashboards in Tableau

Basic Chart Type, Excel Data to Visualisation, Data Visualisation on Transformed Data using SQL, Steps to create basic charts (Bar Chart, Pie Chart, Line Chart, Dual Axis Chart, Stacked Area Chart, Scatter Plot Chart, World Map Visualisation), Creating Dashboards(Filters & Highlighting).

   Session 21 - Excel Introduction & Function

Mathematical Function: Sum, Sumif, Sumifs, Count, Counta, Countblank, Countif, Countifs, Average, Averagea, Averageif, Averageifs, Subtotal, Aggregate, Rand, Randbetween, Roundup, Rounddown, Round, Sumproduct Time Function: Date, Day, Month, Year, Edate, Eomonth, Networkdays, Workday, Weeknum, Weekday, Hour, Minute, Second, Now, Today, Time

   Session 22 - Text Function & Data Validation

Char, Clean, Code, Concatenate, Find, Search, Substitute, Replace, Len, Right, Left, Mid, Lower, Upper, Proper, Text, Trim, Value, Large, Small Filters (Basic, Advanced, Conditional), Sort (Ascending, Descending, Cell/ Font Colour), Conditional Formatting, Data, Validation, Group & Ungroup, Data split.

   Session 23 - Statistical and Other Functions

Isna, Isblank, Iserr, Iseven, Isodd, Islogical, Isytext, Max, Min, Len, Right, Left, Mid, ,Maxa, Maxifs, Median, Minifs, Mina, Vara, Correl, Geomen Logical Functions:- And, Or, If, Iferror, Not, Nested If Lookup & Reference Functions:- VLookup, HLookup, Index, Match, Offset, Indirect, Address, Column, Columns, Row, Rows, Choose, Arrays Concept In Lookup Formula’s, Past Special, Paste link

   Session 24 - Charts & Dashboards in Excel

Pivot Table and Charts, Import and Export data, Protect/Unprotect sheets/workbooks. Worksheet formatting and Print Display, Data Collection Method With Data Quality, Collaboration & Security Like Share Your Workbook On Share Drive With Quality Analysis - Single/Multidimensional Analysis, Like Three Dimensional (3D) Tables, Sensitive Analysis Like Data Table, Manual What-If Analysis, Threshold Values, Goal Seek, One-Variable Data Table, Two-Variable Data Table Advanced Chart Technique, How To Make Dynamic Charts, Bar Charts, Pie Charts, Scatter Chart, Line Chart, Column Chart, Speedometer Chart, Gantt Chart. Advanced Dashboard & Report Development

   Session 25 - Charts & Dashboards in Excel

Overview of SQL, Installing the Test environment, Editors & Platform to Learn SQL, Complete SQL in Class, Using the basic SELECT statement, Selecting rows, Selecting columns, Counting rows, Inserting data, Updating data, Deleting data, Fundamental of SQL.

   Session 26 - Introduction to SQL

Overview of SQL, Installing the Test environment, Editors & Platform to Learn SQL, Complete SQL in Class, Using the basic SELECT statement, Selecting rows, Selecting columns, Counting rows, Inserting data, Updating data, Deleting data, Fundamental of SQL.

   Session 27 - Function in SQL

Numbers and SQL, About numeric types, Finding the type of a value, Integer division and remainders, Rounding numbers, Dates in SQL, Use of Dates and times, Date- and time-related functions, Aggregates Functions, How, aggregates work, Using aggregate functions, Aggregating DISTINCT values, Exploring SQL Transactions, Why use transactions?, Using transactions

   Session 28 - Triggers & View in SQL

Studying Triggers in SQL, Updating a table with a trigger, Preventing automatic updates with a trigger, Automating timestamps with a trigger, What are Subselect and Views in SQL, Creating a simple subselect, Searching within a result set, Creating a view, Creating a joined view, A Simple CRUD Application in SQL, Touring the CRUD application, The SELECT functions, The INSERT, UPDATE, and DELETE functions

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Program Highlights
Live interactive and classroom training will include the practical approach and assessments on regular basis.
Live Case Study
Domain Specific Live interactive case study using industry specific data, problem statements, solution architecture.
Assignments which helps in conceptual understanding, Prepared for interview training & techniques.
Lifetime Access
Class recording, Study material ppt’s, pdf, assignments, datasets, case studies can be accessed through out the lifetime.
24 X 7 Support
We will support If any concern is raised related to the training, assignments, projects, case studies & interview questions.
After successful completion of the training and case study Palin will certify you as Palin certified Data Scientist.
Career Counselor
Avail career guidance and Professional guidance for resume building, unlimited opportunities and interviews.
Certification Preview

Highly motivated with leadership skills having Master’s Degree in Statistics. Holds good working experience in different domains like Retail, Banking, Healthcare FMCG, Marketing analytics using SAS, R, Python, STATA.
   Who can go for Data Science?
Any graduate can go for data science, its independent of qualification. Its more over towards logical rather than technical. We will use more conceptual knowledge rather than the technical knowledge in data science. So who so ever have good logical skills can go for data science.
   What is data science?
Data science is a process of exploring large, complex and varied data sets to reveal hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations to take calculated risks.
   What is future of data scientists?
Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020. By 2020, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM.
   Pre-requisite for data science?
There is no such prerequisite for data science you might have seen that bachelor's degree in statistics and machine learning are mostly data scientists but it is not a prerequisite to learn data science. However if we familiar with the basic concepts of Math and Statistics like Linear Algebra, Calculus, Probability, etc. will help to learn data science.
   Is this a classroom training or online?
In both of the modes Palin arrange classes, can go in either of that classroom as well as online.
   What are the tools we cover in data science?
We'll cover whole process of data science like initially if we want to fetch some data from traditional database we should have the knowledge of SQL. After fetching Data for data mining, data cleaning or data preparation process Need Excel. After that we import that data into either SAS, R or python, after that for predictive analytics will cover basic stats or modeling techniques. will cover some machine learning algorithms. For report presentation n dashboards will use either clikview or tableau. So data science includes SQL, Excel, SAS, R, Predictive Analytics, Machine learning and tableau.
   Which one is better classroom or online?
Everyone have their own opinion towards classroom or online training. In my point of view online classes are far better than classroom session. Online trainings have more much benefits comparative to classroom like we can easily record the session we can access that session whenever we want. In online session we just need a laptop and internet connection can easily access the classroom.
   After the course for which companies I can apply for?
Most of the organizations are working on data analytics platform Like Mckinsey, Delloitte, mercer, TCS, Accenture, Cognizant, IBM, Amex, RBS, UHG, WNS and many more..
   What are pay packages for fresher in Data Science?
An Entry-Level Data Scientist, IT earns an average salary of Rs 579,714 per year. The highest paying skills associated with this job are Machine Learning, Python, and SQL.
   What is the process for data science?
Will cover whole process of data science like initially if we want to fetch some data from traditional database we should have the knowledge of SQL. After fetching Data for data mining, data cleaning or data preparation process Need Excel. After that we import that data into either SAS, R or python, after that for predictive analytics will cover basic stats or modeling techniques. will cover some machine learning algorithms. For report presentation n dashboards will use either clikview or tableau. So data science includes SQL, Excel, SAS, R, Predictive Analytics, Machine learning and tableau.


    I have joined Palin for Analytics training, This type of practical oriented class room training is very helpful for everybody and who joins here for any analytical training with practical orientation approach regularly ensures that they will get exact concept about subject in short period of time. Finally in my words; “Palin Analytics” is the best for learning any Analytical or Data Science Training.

    The first thing I like in Palin is their infrastructure & friendly environment. When someone wants to join for a new course in Analytics you will be given complete knowledge by their experts. This helps in better understanding about what you want from the trainer. Only when you both mutually agree the training will be started. Everything will be clearly informed to you well before the start of training which is very transparent. There is no false promise like they will get you a job like that, but once your training is done & you clear the mock test, they will conduct your interviews. They will provide you enough knowledge on the course. I’ve completed Data Science training from Palin & I’m fully satisfied with their training modules & delievered knowledge.
    Thanks Palin !!

    I was looking for a data science training in Delhi/ncr then I found Palin Analytics, it helped me to get placed in an MNC with that certain skill set they provided me.
    Thanks Palin Team !!

    This was, by far, the Excellent Data Science training course I have attended… Now, I feel prepared to dive into Data Analytics as my career with a solid understanding of the basics. I know this is going to make my life easier over the next year. Thank you!”

    Enrolling for the Data Science at PALIN has been a very good choice as the organization has provided an extensive and in-depth knowledge on the industry exposure. The classes and the study materials have been very helpful in understanding all these analytical tools in a better way. Our trainer was truly the best at delivering the knowledge his way. He always ensured that all our queries are answered and no student be left with a single doubt, Special mention for all the support and guidance and for always being available. Finally switched my profile in analytics in a reputed brand. Thank you Team Palin.

    Nice faculty.. Go for data science course worth it. Value for money and nature of staff is very nice. Kudos!

    PALIN is the best training organization for both class room & online training in Analytical Solutions .Trainers are very skilled Experts and practical knowledge training they have provided was really awesome and I will surely recommend to people who need to speedup with Data Science related Training. They helped me in resuming preparation and Reference interviews. Thank you Palin !!

    Palin is the best Data Science Training Organisation in Delhi/NCR for professionals to Get trained and get placed.
    Trainers at Palin are from industry and their way to deliver the methodologies is too good.
    Thank you Palin.
    Strongly Recommended 👍🏻

    No doubt that Palin Analytics is the best analytics training institute. Case Studies & Real time data sets are provided by them for the specified domain.
    Best placement assistance record.
    Thanks Palin !! 🙂

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