Data Science

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About The Program
Data science program 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….Our main focus would be on Linear Regression, Logistic regression, Predictive analytics like Time series.
Program Description
Data science 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
Mathematical Functions:- Sum, Sumif, Sumifs, Count, Counta, Countblank, Countif, Countifs, Average, Averagea, Averageif, Averageifs, Subtotal, Aggregate, Rand, Randbetween, Roundup, Rounddown, Round, Sumproduct

Date & Time Function:- Date, Day, Month, Year, Edate, Eomonth, Networkdays, Workday, Weeknum, Weekday, Hour, Minute, Second, Now, Today, Time

Text Functions & 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 Color), Conditional Formatting,Data Validation, Group & Ungroup, Data split.

Statistical Function & 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, Past link

Pivot Table and Charts:-Pivot Table and Charts Import and Export data, Protect/Unprotect sheets/workbooks. Worksheet formatting and Print Display

Data Collection:- Data Collection Method With Data Quality, Collaboration & Security Like Share Your Workbook On Share Drive With Quality

SQL : Introduction to SQL, SQL Course overview, Installing the test environment, What is SQL?, Editors and Platforms to learn SQL, Complete SQL in a Class, Quick-start introduction, Using the basic SELECT statement, Selecting rows, Selecting columns, Counting rows, Inserting data, Updating data, Deleting data

Fundamentals of SQL : Databases and tables, SQL syntax overview, Creating tables, Deleting a table, Inserting rows into a table, Deleting rows from a table, What is NULL?, Controlling column behaviors with constraints, Changing a schema with ALTER, Creating an column with ID, Filtering data with WHERE, LIKE, and IN, Removing duplicates with SELECT DISTINCT, Sorting with ORDER BY

Joins : How Relationships Work in SQL, Understanding joins, Accessing related tables with JOIN, Multiple related tables,

Strings : Explaining SQL Strings, About SQL strings, Finding the length of a string, Selecting part of a string, Removing spaces with TRIM, Making strings uppercase and lowercase

Functions : 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

Triggers & views: Studying Triggers in SQL, Updating a table with a trigger, Preventing automatic updates with a trigger, Automating timestamps with a trigger, What are Subselects 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

SAS:- Introduction to SAS Programming Working in the SAS Environment,Introduction, Working with the windows,Program editor window, Log window, Output window, Result window, Explorer window

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 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 into SAS can be put into four general categories, Entering data directly into SAS data sets, Creating SAS data sets from raw data files, Converting other software’s data files into SAS data sets, Reading other software’s data file directly, Input Styles, List input, Column input, Formatted input, Modified/Mixing input, Reading Messy Raw Data, Reading Multiple lines of Raw Data per observation, Reading Multiple observations per line of raw data, Reading delimited file with DATA step, Assigning Variable Attributes, Permanent Attributes, Temporary Variable Attributes, Pointers, Column pointers, Line Pointers

Formats & Informats : Character informats, Numeric informats Date and time informats, Character formats, Numeric formats, Date and time formats

Functions : Arithmetic Functions,String Functions,Date and Time Functions

Conditional Statements : Reading Raw Data from External File, Infile statement, Import, Export, Options, Global options, Local Options, Statements, Global Statements, Local Statements, Control Statements, If statement and if else statement, If then else statement, Where statement, Loops (do, dountil, dowhile)

Joins : Retrieving Data from Multiple tables, Natural Join, Inner Join, Outer Join (Right, Left, Full)

Procedures :Proc Print, Proc Transpose, Proc Sort, Proc Contents, Proc Formats, Proc Append, Proc Tabulate, Proc Import, Proc Report, Proc Export, Proc Datasets, Proc Freq, Proc Means, Proc Reg, Proc Annova,

Combining SAS Datasets : Concatenating, Merging, One-to-one merging, One-to-many merging, Many-to-one merging, Many-to-many merging, Matching merging, Updating, Debugging of Errors, Writing SAS programs that work, Fixing programs that Don’t work, Searching for the Missing Semicolon

Fundamentals : 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

Visualization : 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

Data Preperation : 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

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

Modeling Techniques : Predictive modelling in SAS & R, Correlation, Simple linear regression, Multiple linear regression, Model assumptions, Model diagnostics and validation, Moving from linear to logistic, Logistic regression, Odds ratio, Model assessment and gains table, ROC curve and KS statistic, Techniques of customer segmentation, Need for segmentation, Criterion of segmentation, Types of distances, Clustering algorithms, Hierarchical clustering, K-means clustering, Deciding number of clusters, Time series forecasting techniques, Need for forecasting, What are time series?, Smoothing techniques, ARIMA, Time series models.

Decision trees: What are decision trees, Entropy and Gini impurity index, Decision tree algorithms, CART & CHAID, Machine Learning Algorithms Random forest, Support Vector Machine, Knn classification etc.

Text Analytics : Sentimental Analysis, predictive modelling on Text Data, Social Media analysis (Twitter, linkedIn, facebook)

Tableau : Install Tableau Desktop, Connect Tableau to various Datasets: Excel and CSV files, Create Barcharts, Create Area Charts, Create Maps, Create Scatterplots, Create Piecharts, Create Treemaps, Create Interactive Dashboards, Create Storylines, Understand Types of Joins and how they work, Work with Data Blending in Tableau, Create Table Calculations, Work with Parameters, Create Dual Axis Charts, Create Calculated Fields, Create Calculated Fields in a Blend, Export Results from, Tableau into Powerpoint, Word, and other software, Work with Timeseries Data (two methods), Creating Data Extracts in Tableau Understand Aggregation, Granularity, and Level of Detail, Adding Filters and Quick Filters, Create Data Hierarchies, Adding Actions to Dashboards (filters & highlighting)

Program Highlights
Instructor-led Session
Instructor led training is the live and interactive training by industry professional who have multiple years experience in different domains. This training will include the practical approach and assessments on regular basis. Trainers will guide you how to follow an appropriate approach.
Real-life Case Study
Case studies are the live projects you will be working on where every candidate is provided with the data from different domains like Banking, Healthcare, ecommerce and retail etc. So that he can perform analysis and generate reports for the same. By doing these case studies every candidate will get a practical experience of how analytics is done for decision making.
Assignments
For every session our experts have designed some assignments for the candidates so that they could clear their conceptual understanding of each topic. Most of these assignments are prepared keeping in mind the interview training n techniques so that it can also help candidate during placements.
Lifetime Access
Every session is recorded and the recording is shared with the candidates for the life time so that they can refer to them in case of any doubts or need to repeat the sessions and these recordings will be in high quality mp4 format shared via email to candidates.
24 X 7 Expert Support
Apart from the training our support team is available 24×7 to provide answers to your queries about this training and resolve problem statements made by you in any of the analytical tool by industry experts.our team of client support is ready to serve you at any time of the day.
Certification
Palin certified data science trainees are working in many MNCs acknowledged by industry experts and globally appreciated. This 3 months certification program can yield you a career into business analytics which you may dream of. A certificate from Palin is what you earn by clearing rounds of assessments.
FAQ’s
Data science is the sexiest job of 21 st century and it’s the high time because every sector is in shortage of data scientists. It has opportunities in many domains like ecommerce, healthcare, banking, social media, search engines etc. It has many profiles for fresher as well as experienced professionals and has profiles to the higher hierarchy.
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.
Palin analytics focuses on industry specific course content and our course material is designed by industry experts with minimum of 8 years experience in relevant profiles. We do provide PPTs for each session as well as assignments and reference codes so that you get prepared each time you enter the session and can maximize your chances of understanding concepts better.
We believe in providing you the training only by industry working professionals with ample amount experience in relevant profiles so that they can impart theoretical as well as current scenarios in the industry to you which makes it easy for you to get hands on experience on the profile and not only the tool knowledge.
You can directly register through our website with a registration link or you can directly submit your fee via check or in cash at Palin gurgaon head office. We also accept fee through payTM and payzap.
Data science is made up of three constituents programming, statistics and business. If you are comfortable with any two, it will be easy to adapt to the third one. Apart from this you need a bachelor’s degree and masters will be an add in for you and you are good to go. Best of luck!!
This training program is a completely lab based practical training and we have divided it in hours specified for each tool and you will complete a case study as part of your project in last so that you can get a hands on experience in Data analytics. By doing that you will get to work on industry specific data and you can easily claim to be a Data Scientist.
We do provide physical classroom and online meeting training and both are live and instructor led and there is nothing like recorded sessions in it. As time is a constraint for many professionals and those who are far from gurgaon and don’t prefer travelling our online sessions turned out to be a savior as they get to learn at home and receive the whole session recorded back up as well so that they don’t even to maintain notes and can access their previous classes anytime anywhere.
Fresher are getting lot of opportunities these days into data science as they offer scalability and thinking out of the box which is required by most of the organizations. As a fresher you can expect a minimum of 4-6 LPA though right candidates have no bar.
We are working as consultancy as well as training organization. We have our aluminize working in different sectors in market analytics giants and you can be their successors by following the path they did. We provide you a platform where you can learn and prove your worth to the organizations.
As a trainee you will do case studies in 3 different domains at the end of your training. These domains may be healthcare, ecommerce, banking, finance etc. These case studies will give an industry exposure and prepare you for the upcoming challenges in analytics.