By embracing data science tools and technologies, companies can more effectively inform strategic decision-making, reducing uncertainty and eliminating analysis-paralysis.
What is RDBMS?
What is SQL?
ü What is SQL
ü SQL Process
ü SQL Architecture
ü Data Definition Language
ü Data Manipulation Language
ü Data Control Language
ü Data Query Language
Fundamental of SQL
ü Creating Database
ü Creating Tables
ü Insertion of rows
ü Deletion of rows
ü NULL Value
ü Not NULL
ü Removing Duplicate data
ü Sorting data
ü Alteration of Data
ü Default Constraints
ü Primary Keys
ü Foreign Keys
ü Composite Keys
ü Select Statement
ü Distinct Clause
ü Where Clause
ü AND/OR Clause
ü Arithmetic Operators
ü Comparison Operators
ü Logical Operators
ü Relationships in SQL
ü Understanding Joins
ü Types of Joins
· Inner Join
· Outer Join
· Right Join
· Left Join
· Full Join
· Self Join
· Cartesian Join
ü Concept of Keys
ü Brief about Function
SQL Union Clause
ü The Union All Clause
SQL Alias Syntax
SQL Truncate Table
SQL Using Views
ü Performing dataset with MYSQL
ü Introduction to R Programming
ü Data Types in R
ü Functions in R
ü Summarizing data by using various functions
ü Indulge into a class activity to summarize the data
ü Various subsetting methods
ü Data import technique in R
ü Import data from spreadsheets and text files into R
ü Install packages used for data import
ü Connect to RDBMS from R using ODBC and basic SQL queries in R
ü Perform basic web scrapping
Data Manipulation in R
ü Know the various steps involved in data cleaning
ü Functions used for data inspection
ü Tacking the problem faced during data cleaning
and when to use functions like grep, grepl, sub, gsub,
regexpr, gregexpr, strsplit
ü How to coerce the data
ü What is data exploration
ü Data exploring using Summary(), mean(), var(), sd(), unique()
ü Using Hmisc package and using summarize, aggregatefunction
ü Learning correlation and cor() function and visualizing the same using corrgram
ü Visualizing data using plot and its different flavours
ü Dist function
ü Gain understanding on data visualization
ü Learn the various graphical functions present in R
ü Plot various graph like tableplot, histogram, boxplot etc.
ü Customize graphical parameters to improvise the plots.
ü Understand GUIs like Deducer and R commander
ü Introduction to spatial analysis.
Fundamentals of Statistics
ü Basic statistics; descriptive and summary
ü Inferential statistics
ü Statistical tests
Data Prep and Reduction techniques
ü Need for data preparation
ü Outlier treatment
ü Flat-liners treatment
ü Missing values treatment
ü Factor Analysis
ü Statistics Basics Introduction to Data Analytics and Statistical Techniques
ü Types of Variables, measures of central tendency and dispersion
ü Variable Distributions and Probability Distributions
ü Normal Distribution and Properties
ü Central Limit Theorem and Application
ü Hypothesis Testing Null/Alternative Hypothesis formulation
ü One Sample, two sample (Paired and Independent) T/Z Test
ü P Value Interpretation
ü Analysis of Variance (ANOVA)
ü Chi Square Test
ü Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)
Introduction to Business Analytics
ü Relevance in industry and need of the hour
ü Types of analytics – Marketing, Risk, Operations etc.
ü Future of analytics and critical requirement
ü Basics of regression analysis
ü Linear regression
· Simple Linear Regression
· Multiple Regression
ü Logistic regression
ü Interpretation of results
ü Multivariate Regression modeling
ü K-Nearest Neighbours (K-NN)
ü Support Vector Machines
ü Understand what is Decision Tree
ü Algorithms for Decision Tree
ü Decision Tree Classification
ü Greedy approach: Entropy and information gain.
ü A perfect decision tree
ü Understand the concept of random forest
ü Random Forest Classification
ü How random forest work
ü Features of random forest
ü Time series modeling
ü Cross-sell and Up-sell opportunities and modeling
ü Churn prediction models and management
ü Basics clustering
ü Deciles analysis
ü K-Means Clustering
ü Hierarchical Clustering
ü Cluster evaluation and profiling
ü Interpretation of results
ü Introduction to Data Visualization with Tableau
ü Data Import and Management
ü Data Type and Operation
ü Visualizations Deep Dive
ü Data Organization and Scripting
ü Playing with Time Dimension
ü What is Your Location?
ü Incremental Loading and Blending
ü The World is Your Visualization
ü Statistical Analysis with Tableau & R
ü Sharing Insights with Enterprise Dashboards
ü Analyse Project data and extract meaningful information from it
ü Create Dashboards and Stories from data sets
Project: A dashboard should be created that would contain
ü A list of Orders returned by the Customers (in terms of refund).
ü Top 10 Countries mapped on the World Map that had most of the refunds.
ü Predict the refund for next 1 year category-wise.
ü Actions to be Performed to Create the Dashboard
ü Create hierarchies and folders in the dataset provided.
ü Generate a list of order returned from customers and compare it to the original sales, sort the visualization in ascending.
ü Order in terms of returned order, for top 10 countries in terms of refunds.
ü Map top 10 countries, with most of the refunds on the world map.
ü Predict the returned sale with lowest and actual forecast of the data
ü Add a URL action to represent details of the countries
ü Finally publish your work to Tableau Server/ Online
(Live Project to get hands on tools with one of these specified domains)
Any graduate who’s good at maths or stats and willing to work on Advance Modeling with R as Data Analyst, Business Analyst etc can go for Data Science using R training.
Advance modeling techniques in R like Linear & Logistic regression, classification algorithms, clustering are used to analyse large data and helps bringing inference and decision making for different business needs.
When it comes to data science, R is a great language to master. First of all, it’s a language friendly to beginners — there are certain features in the language that make it easy to get started and develop prototypes quickly.
Candidates who are good at maths or stats is an advantage for them. Any Graduate/diploma candidate can apply for this training and get mentored by the experts of the industry.
Palin Analytics offers you both the training modes. You can opt either classroom or online.
Both the training modes are good to get trained. But online training is more better than the classroom training because online trainings are live interactive where you can raise your concerns at any point during the session. Additionally recording of the same session will be provided which you can access anytime anywhere.
R Analytics includes Basic Fundamental of R, Data Manipulation in R, Data import techniques in R, Data Exploration, Data Visualisation in R, Data Mining, Linear & Logistic Regression, ANOVA, Z-Test, Hypothesis Testing, Cluster Analysis, Customer Segmentation, Time Series Analysis, Decision Tree and Random Forest. Real Cases to get hands on working experience on R with Advance Modeling, Reporting and dashboards in Tableau and SQL
Almost every organisation which has a large customer base is shifting to R Analytics. Companies like Amazon, Infosys, TCS, Accenture, IBM, Flipkart, Airtel are highly in need for analytics professionals.
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, R, and SQL.
We'll be starting from Fundamental of R to understand working with files and databases. Then we'll cover advance statistical techniques to get hands on business analytics with regression modeling, classification & unsupervised learning and then we'll cover reporting and dashboarding using tableau and SQL.