DATA SCIENCE

WHAT IS DATA SCIENCE?

Data Science course is the professional qualification that shows the ability of the candidate to attain complete subject knowledge and learn all the basic tools and algorithms used in Data Science. This course will make the student get the leading job posts in the MNC. This course is offered to you with the necessary skills required to start your career in the Data Science industry. With the help of this certification, you can make a positive impact on yourself during the interview and you can grab the job opportunity with ease. You will gain the core knowledge of the major services in this field. The aspirants who are looking to kick start their career in the Data Science can take up this Data Science Course in Mumbai at Wings Academy which leads to a successful path to their career.

Data Science with Python Training Syllabus

Introduction to Data Science

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types




 

Introduction to Python

• What is Python?
• Why Python?
• Installing Python
• Python IDEs
• Jupyter Notebook Overview




 

Python Basics

• Python Basic Data types
• Lists
• Slicing
• IF statements
• Loops
• Dictionaries
• Tuples
• Functions
• Array
• Selection by position & Labels




 

Python Packages

• Pandas
• Numpy
• Sci-kit Learn
• Mat-plot library




 

Importing data

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to csv file




 

Manipulating Data

•  Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques




 

Statistics Basics

• Central Tendency
• Mean
• Median
• Mode
• Skewness
• Normal Distribution
• Probability Basics
• What does mean by probability?
• Types of Probability
• ODDS Ratio?
• Standard Deviation
• Data deviation & distribution
• Variance
•  Bias variance Trade off
• Underfitting
• Overfitting
• Distance metrics
• Euclidean Distance
• Manhattan Distance
• Outlier analysis
• What is an Outlier?
• Inter Quartile Range
• Box & whisker plot
• Upper Whisker
• Lower Whisker
• catter plot
• Cook’s Distance
• Missing Value treatments
• What is a NA?
• Central Imputation
• KNN imputation
• Dummification
• Correlation
• Pearson correlation
• Positive & Negative correlation
• Error Metrics Duration-3hr
• Classification
• Confusion Matrix
• Precision
• Recall
• Specificity
• F1 Score
• Regression
• MSE
• RMSE
• MAPE




 

Machine Learning

Supervised Learning

• Linear Regression
• Linear Equation
• Slope
• Intercept
• R square value
• Logistic regression
• ODDS ratio
• Probability of success
• Probability of failure
• ROC curve
• Bias Variance Tradeoff




 

Unsupervised Learning

• K-Means
• K-Means ++
• Hierarchical Clustering
• ther Machine Learning algorithms
• K – Nearest Neighbour
• Naïve Bayes Classifier
• Decision Tree – CART
• Decision Tree – C50
• Random Forest




 

Data Science with SAS Training Syllabus

• verview of SAS
• Introduction and History of SAS
• Significance of SAS software solutions in various industries
• Demonstrate SAS Capabilities
• Job Profile / career opportunities with SAS worldwide?




 

Base SAS Fundamentals

• Explore SAS Windowing Environment
• SAS Tasks
• Working with SAS Syntax
• Create and submit a SAS sample program




 

Data Access & Data Transformation

• Accessing SAS Data libraries
• Getting familiar with SAS Data set




 

Reading SAS data set

• Introduction to reading data
• Examine structure of SAS data set
• Understanding of SAS works




 

Reading Excel worksheets

• Using Excel data as input
• Create as sample program to import and export excel sheets




 

Reading Raw data from External File

• Introduction to raw data
• Reading delimited raw data file (List Input)
• Using standard delimited data as input
• Using nonstandard delimited data as input
• Reading raw data aligned to columns (Fixed or column input)
• Reading raw data with special instructions (Formatted input)




 

Writing to an External file

• Write data values from SAS data set to an external file




 

Data transformations (Data step processing)

• Create multiple output datasets from single SAS dataset
• Writing observations to one or more SAS datasets
• Controlling which observations and variables to be written to output data




 

Creating subset of observations using

• Where condition
• Conditional processing using: IF statements




 

Processing Data Iteratively

• Iterative DO loop processing with END statement
• DO WHILE & DO UNTIL Statement
• SAS Array statement




 

Summarizing data

• Creating and Accumulating total variable (Retain)
• Using Assignment statement
• Accumulating totals for a group of data (BY group)




 

Manipulating Data

• Sorting SAS data sets
• Manipulating SAS data values
• Presentation of user defined values /data/currency values using FORMAT procedure
• SAS functions to manipulate char and num data
• Convert data type form char-to num and num-to-char
• SAS variables lists/ SAS variables lists range
• Debugging SAS program
• Accessing observations by creating index




 

Restructuring a SAS data set

• Rotating with the data step
• Using the transpose procedure




 

Combining SAS data sets

• Concatenation
• Interleaving
• One to one reading
• One to one merging (with non-matching)
• Match merging (Merging types with IN=option)




 

SAS Access & SAS Connect

• Validating and cleaning data
• Detect and correct syntax errors
• Examining data errors




 

Analysis & Presentation

• SAS/REPORTS SAS/GRAPH
• SAS/STATS SAS/ODS




 

Producing detailed /Summary Reports

• Freq Report
• Means Report
• Tabulate Report
• Proc report
• Summary report
• Univariate report
• Contents report
• Print report
• Compare proc
• Copy proc
• Datasets proc
• Proc append
• Proc delete




 

Generating Statistical Reports using

• Regression proc
• Uni/Multivariate proc
• Anova proc




 

Generating Graphical reports using

• Producing Bar and Pie charts (GCHART Proc)
• Producing plots (GPLOT Proc)
• Presenting Output Report result in:
• PDF
• Text files
• Excel
• HTML Files




 

SAS/SQL Programming

• Introduction and overview to SQL procedure
• Proc SQL and Data step comparisons




 

Basics Queries

• Proc SQL syntax overview
• Specifying columns/creating new columns
• Specifying rows/subsetting on rows
• Ordering or sorting data
• Formatting output results
• Presenting detailed data
• Presenting summarized data




 

Sub Queries

• Non correlated sub queries
• Correlated sub queries




 

SQL Joins (Combining SAS data sets using SQL Joins)

• Introduction to SQL joins
• Types of joins with examples
• Simple to complex joins
• Choosing between data step merges and SQL joins




 

SET Operators

• Introduction to set operations
• Except/Intersect/Union/Outer union operator




 

Additional SQL Procedures features

• Creating views with SQL procedure
• Dictionary tables and views
• Interfacing Proc SQL with the macro programming language
• Creating and maintaining indexes
• SQL Pass-Through facility




 

SAS Macro Language

• Introduction to macro facility
• Generate SAS code using macros
• Macro compilation
• Creating macro variables
• Scope or macro variables
• Global/Local Macro variables
• User defined /Automatic Macro variables
• Macro variables references
• Combing macro variables references with text
• Macro functions
• Quoting (Masking)
• Creating macro variables in Data step (Call SYMPUT Routine)
• Obtaining variable value during macro execution (SYMGET function)
• Creating macro variables during PROC SQL execution (INTO Clause)
• Creating a delimited list of values
• Macro parameters
• Strong Macro using Autocall Features
• Permanently storing and using stored compiled macro program
• SAS Macro debugging options to track problems




 

Basics Statistics

• Standard deviation
• Correlation Coefficients
• Outliers
• Linear regressions
• Clustering
• Chi Square




 

Trainer Profile of Data Science Training

Our Trainers provide complete freedom to the students, to explore the subject and learn based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates are free to ask any questions at any time.

• • More than 10+ Years of Experience.
• • Strong Theoretical Practical Knowledge.
• • Data Science Certified Professionals with High Grade.
• • Expert level Subject Knowledge and fully up-to-date on real-world industry applications.
• • Trainers have Experienced on multiple real-time projects in their Industries.
• • Our Trainers are working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies etc
• • Strong Theoretical & Practical Knowledge.
• • Certified Professionals with High Grade.
• • Expert level Subject Knowledge and fully up-to-date on real-world industry applications.
• • Trainers have Experienced on multiple real-time projects in their Industries.