Call :+91 8055223360 

ENROLL NOW

DATASCIENCE WITH S-A-S & PYTHON TRAINING IN KOCHI

1231 Ratings
☆☆☆☆☆ 5/5
2613 Learners

Duration : 100 Hrs | 5 Major Projects | 10 Minor Projects | 100 + Assignments

Data Sets, installations, Interview Preparations, Repeat the session until 6 months are all attractions of this particular course

Trainer:- Experienced Data Science Consultant

Enquire Now

Overview

This course starts with  Data Science and Statistics using Python and then complete knowledge of Data Science with S-A-S. It covers both the aspects of Statistical concepts and the practical implementation using  S-A-S. If you’re new to programming, don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new to Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PCs; the sample code will also run on Mac OS or Linux desktop systems.

Watch
INTRO VIDEO

Why Radical Technologies

  • Highly practical oriented training
  • 25000+ Man-hours of Real-time projects & scenarios
  • 10 to 20+ year Experienced corporate trainers With Real Time Experience.
  • Building up professionals by highly experienced professionals
  • 100 % quality assurance in training .
  • 10000+ Placement Records and 180+ MNC’s and Consultancies Tie up

Check Batch Schedulings

Batch Schedule

Course Curriculum

Course description

 This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualize your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

Course Content

  • Analytics World
    • Introduction to Analytics
    • Concept of ETL
    • S-A-S in advanced analytics
  • Global Certification: Induction and walk through
    • Getting Started
    • Software installation
    • Introduction to GUI
    • Different components of the language
    • All programming windows
    • Concept of Libraries and Creating Libraries
    • Variable Attributes – (Name, Type, Length, Format, In format, Label)
    • Importing Data and Entering data manually
  • Understanding Datasets
    • Descriptor Portion of a Dataset (Proc Contents)
    • Data Portion of a Dataset
    • Variable Names and Values
    • Data Libraries
  • Understanding Data Step Processing
    • Data Step and Proc Step
    • Data step execution
    • Compilation and execution phase
    • Input buffer and concept of PDV
  • Importing Raw Data Files
    • Column Input and List Input and Formatted methods
    • Delimiters, Reading missing and non standard values
    • Reading one to many and many to one records
    • Reading Hierarchical files
    • Creating raw data files and put statement
    • Formats / Informat
  • Importing and Exporting Data (Fixed Format / Delimited)
  • Proc Import / Delimited text files
  • Proc Export / Exporting Data
  • Datalines / Cards;
  • Atypical importing cases (mixing different style of inputs)
    • Reading Multiple Records per Observation
    • Reading “Mixed Record Types”
    • Sub-setting from a Raw Data File
    • Multiple Observations per Record
    • Reading Hierarchical Files
    •  
  • Concept of SAS library and SAS Catalog
  • Variable Types in SAS
  • Reading Data stored external to SAS
  • Importing Data by using Proc Import
  • Data Step SAS statements
  • SAS Functions
  • Appending and Merging using SAS
  • SAS Procedures like proc means, proc Univariate, proc append, proc freq, and proc export.
  • SAS SQL
  • SAS Macros
  • One Sample t-test of comparing means
  • Two Sample t-test of comparing means
  • One Way ANOVA
  • Assumptions of ANOVA Modeling
  • n-way ANOVA
  • ANOVA Post Hoc Studies
  • Apply the principles of honest assessment to model performance measurement
  • Assess classifier performance using the confusion matrix
  • Model selection and validation using training and validation data
  • Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
  • Establish effective decision cut-off values for scoring
  • Understanding and Exploration Data
    • Introduction to basic Procedures – Proc Contents, Proc Print
  • Understanding and Exploration Data
    • Operators and Operands
    • Conditional Statements (Where, If, If then Else, If then Do and select when)
    • Difference between WHERE and IF statements and limitation of WHERE statements
    • Labels, Commenting
    • System Options (OBS, FSTOBS, NOOBS etc…)
  • Data Manipulation
    • Proc Sort – with options / De-Duping
    • Accumulator variable and By-Group processing
    • Explicit Output Statements
    • Nesting Do loops
    • Do While and Do Until Statement
    • Array elements and Range
  • Combining Datasets (Appending and Merging)
    • Concatenation
    • Interleaving
    • Proc Append
    • One To One Merging
    • Match Merging
    • IN = Controlling merge and Indicator
  • Introduction to Databases
  • Introduction to Proc SQL
  • Basics of General SQL language
  • Creating table and Inserting Values
  • Retrieve & Summarize data
  • Group, Sort & Filter
  • Using Joins (Full, Inner, Left, Right, and Outer)
  • Reporting and summary analysis
  • Concept of Indexes and creating Indexes (simple and composite)
  • Connecting S-A-S to external Databases
  • Implicit and Explicit pass-through methods
  • Macro Parameters and Variables
  • Different types of Macro Creation
  • Defining and calling a macro
  • Using call Symput and Symget
  • Macros options (mprint symbolgen mlogic merror serror)
  • Basic Statistics – Measures of Central Tendencies and Variance
  • Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
  • Inferential Statistics -Sampling – Concept of Hypothesis Testing
  • Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square    
  • Levels of Measurement and Variable types
  • Descriptive Statistics and Picturing Distributions
  • Confidence Interval for the Mean
  • Introduction to Predictive Modeling
  • Types of Business problems – Mapping of Techniques
  • Different Phases of Predictive Modeling
  • Need of Data preparation
  • Data Audit Report and Its importance
  • Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
  • Variable Reduction Techniques – Factor & PCA Analysis
  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  • Behavioural Segmentation Techniques (K-Means Cluster Analysis)
  • Cluster evaluation and profiling
  • Interpretation of results – Implementation on new data
  • Introduction – Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results – Business Validation – Implementation on new data
  • Introduction – Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve,
    Probability Cut-offs, Lift charts, Model equation, Drivers, etc)
  • Interpretation of Results – Business Validation -Implementation on new data
  • Introduction – Applications
  • Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
  • Classification of Techniques(Pattern based – Pattern less)
  • Basic Techniques – Averages, Smoothening, etc
  • Advanced Techniques – AR Models, ARIMA, etc
  • Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc
  • Statistical learning vs. Machine learning
  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
  • Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
  • Recursive Partitioning(Decision Trees)
  • Ensemble Models(Random Forest, Bagging & Boosting)
  • K-Nearest neighbours
DOWNLOAD SYLLABUS

Training Options

Live Online Training

  • Highly practical oriented training
  • Installation of Software On your System
  • 24/7 Email and Phone Support
  • 100% Placement Assistance until you get placed
  • Global Certification Preparation
  • Trainer Student Interactive Portal
  • Assignments and Projects Guided by Mentors
  • And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Get Started

Live Classroom Training

  • Weekend / Weekdays / Morning / Evening Batches
  • 80:20 Practical and Theory Ratio
  • Real-life Case Studies
  • Easy Coverup if you missed any sessions
  • PSI | Kryterion | Redhat Test Centers
  • Life Time Video Classroom Access ( coming soon )
  • Resume Preparations and Mock Interviews
  • And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Get Started

Exam & Certification

Course Reviews

Why we are the best Radical Technologies

Radical Technologies is truly progressing and offer best possible services. And recognition towards Radical Technologies is increasing steeply as the demand is growing rapidly.

Creative

0%

Innovative

0%

Student Friendly

0%

Practical Oriented

0%

Valued Certification

0%

Training FAQs

Similar Courses

DATASCIENCE WITH S-A-S & PYTHON TRAINING IN KOCHI

Very good learning experience, I am a beginner in DS, but the instructors in this course simplified the contents that made me understand easily , tools and materials were very helpful to start with.

Course Provider: Organization

Course Provider Name: Radical Technologies

Course Provider URL: https://radicals.in/

Editor's Rating:
5

Our Ratings

★★★★★ 5/5

★★★★★ 4.6/5

★★★★★ 4.7/5

★★★★★ 4.8/5

★★★★★ 4.6/5

Radical Technologies is a recognized leader in training of Administrative and Soft ware Development courses since 1995 to empower IT individuals with competitive advantage of exploiting untapped jobs in IT sector

Facebook-f Youtube Instagram Linkedin

Get Started

Contact us

ENQUIRE NOW










    [recaptcha]

    Dismiss ad

    This will close in 0 seconds