DATA SCIENCE Training in Pune-Mumbai-Thane-online-classroom-classes. Taking the Data Scientist training can put you in a different league compared to your peers as you would be having the most-sought after set of skills. Taking the master program in data science like that one offered by GoalsInfoCloud can help you clear the industry certification, work on real world projects and get the best jobs. Data Scientist is the best job of the 21stcentury – Harvard Business Review Global Big Data market to reach $122B in revenue by 2025 – Frost & Sullivan The US alone could face a shortage of 1.4 -1.9 million Big Data Analysts by 2018 – Mckinsey.
DATA SCIENCE Training in Pune: DATA SCIENCE USING R PROGRAMMING
Pre-requisite to course
– Basic Mathematics, Probability fundamentals, Basic Statistics
– Fundamentals to Statistics will be covered
Course Area of applications
– Business Intelligence, Decision support systems, Analytics
DATA SCIENCE USING R PROGRAMMING FOR BUSINESS APPLICATIONS
The data science pipeline is a 4 step process.
A. Data Management (Data Acquisition)
B. Data Visualizations (Data Exploration-EDA)
C. Data Analysis (Building Models)
D. Data Visualizations (Interpretation Building Statistical Web-Applications)
TOPIC : Preparing the DS environment
Setting up configurations using Windows or Linux
– Preparing the DS environment
– Understanding the data science pipeline
– Installing the R programming on windows and Linux
– Install required packages in R for data science
TOPIC 1: (DATA ACQUISITION)
Structure the Unstructured
– Multi-channel data sourcing
– Learn to modify, transform, arrange the original state of the data
– Converting raw data to useful information
– Analyse multi-format files using multiple data input structure
– Take extensive care of required metadata for analysis
The best way to know the science behind a data is to break it down or bring each of its pieces together to create more meaningful information.
• Gather data from a webpage (could be Stock Prices, Commodity Prices, Currency Prices)
• Investigate the data to apply the type of transformations
• Apply complex string calculations to retrieve a part or whole string
• Extensive use of Regular expressions to content filter the text data
• Store the structured data to a data frame
– Transform the acquired data using transformations packages in R programming
– To deal extensively with dplyr package in R to
– Summarise Cases
– Group Cases
– Manipulate Cases
– Arrange Cases / Add Cases
– OFFSETS / CUMMULATIVE AGGREGATES / EXTRACTING ROWS / COMBINING CASES
– To deal extensively with tidy & tidyverse package in R to
– Readable code chunks
– consistent function design
AIM: to decrease development time and to improve code readability and maintainability
Other packages to deal with for data transformations are:
TOPIC 2: (Data Exploration)
Visual & Charting information
– Driving visual analysis with in-built data-set in r programming
– Load different format of data into R programming (CSV, TEXT, SQL)
– Convert variable to different data types
– Transposing a data set
– Sorting data
– Plotting data
– Generating frequency distributions
– Sample data set / Remove duplicate values of a variable
– Treating missing values and outliers
– Merging / joining datasets
– Work with different types of visualizations
– Interpret the descriptive data from the visualizations
– Work with both summarized and statistical charts
– Explore different charting libraries in R programming (ggplot2, plotly)
– Correlation analysis, find the right candidate variable for regression
– Understand various forms of data distributions (Normal, BiNormal)
TOPIC 3: Building Data Models
Building models for TEXT Mining, Market Basket Analysis, Time Series, Clustering & Classification
– Building complete model designed to find the correlations between the character or words defined in a statement or a paragraph.
– Extensive use of the text binding functions
– Analysing term frequencies
– Building corpus of physical text
– Correlations between words
– Converting from non-tidy text to tidy
– Case study on Twitter archives and basic you tube comment analysis
– Conclusion to derive sentimental analysis, topic of discussion from a given set of comments, who is the author of a particular blog etc.
MARKET BASKET ANALYSIS
– Collecting Grocery Store data set to apply Association Rules
– Applications: Product Recommendation, Music Recommendation, Content Optimizations
– Understanding the data set and frequently bought items
– Understand Support, Confidence and Lift charts
– Apriori Recommendation with R
– Sorting, Redundancies and Target items
– Creating time series object
– Smoothing and Decomposition
– To understand the type of time series we are dealing with (Linear, Non-Linear, Stationary, Non-Stationary)
– Does the time series depict a pattern, a trend or any type of seasonality
– Understand different types of statistical tests to check if the given time series is stationary
– To check if given series is additive in nature or multiplicative
– Rules to check and apply if model needs additive method or multiplicative
– Exponential Forecasting Models
– When to use exponential forecasting model
– When not to use exponential forecasting model
– Other types forecasting models
– ARIMA Forecasting Models
– To understand the LAG analysis and their correlation to observed values in a given time series
– To understand the Autocorrelation Functions and Partial Autocorrelation Function
– To derive correlation coefficients or weights i.e. P, D and Q values using the ACF & PACF
– Understand the significance of Residual analysis (to model with better accuracy)
– Accuracy checks to confirm model design or to consider re-design
– To fit the perfect model and Forecast using new values
– Collect different formats of time series data and use different forecasting methods to predict.
– Understand non-supervised way of machine learning algorithm
– Building blocks to create a cluster solution
– Types of Clustering – Hierarchical, k-means
– Differences between Hierarchical and k-means clusters
– Applications of clustering
– Examples on Segmenting data
– Understand supervised way of machine learning algorithm
– Understand different techniques to build classification models
– Types of classification – Statistical classification, naïve bayes classifier, Decision trees
– Simple example data set to apply and building models – Recommendation
TOPIC 4: Building Statistical Web-Applications
– The end goal of data transformation and analysis is user experience
– Building UX in terms of statistical representations using modern web browsers
– Build guided analytical web application for decision makers
– Focus on Agile way of building web-applications
– Information delivery / learning SaaS building blocks
– Information on the cloud
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