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.
₹39,500.00
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.
₹39,500.00
Data Science Discussion, Profile Discussion, Domain Discussion, Tools Discussion
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.
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).
Arithmetic Functions, Reading Raw Data from External File, Infile statement, Import, Export, Options, Global options, Local Options, Statements, Global Statements, Local Statements
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)
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)
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.
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 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.
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.
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.
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.
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.
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.
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.
Inter variable dependence, Hierarchical Clustering, k means clustering, Principal Component Analysis(PCA), Data Reduction, Eigen values, Eigen vectors, Assumption of PCA, Interpretation of results
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.
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.
Business Overview, Introduction to Data Visualisation, Introduction to Business Intelligence, Data Visualization tools, Data Connection, Tableau Server & Tableau Reader, Tableau Features
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).
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
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.
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
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
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.
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.
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
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
Khushboo Thareja –
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Shweta Garg –
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.
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Ankit –
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.
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Neha Jain –
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!”
Ashima Thareja –
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.
Jatin Goyal –
Nice faculty.. Go for data science course worth it. Value for money and nature of staff is very nice. Kudos!
Sonia Mehta –
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 !!
Lovely Mehta –
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.
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Manisha –
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.
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