Syllabus of Data Science Course in Chennai
Besant Technologies boasts of being the Best Data Science training institute in Chennai. And the syllabus for this course has been broken down below (not in any specific order) for your convenience.
Data Science with Python Training Syllabus
Module 1: Introduction to Data Science (Duration-1hr)
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
Module 2: Introduction to Python (Duration-1hr)
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
Module 3: Python Basics (Duration-5hrs)
- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
Module 4: Python Packages (Duration-2hrs)
- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
Module 5: Importing data (Duration-1hr)
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
Module 6: Manipulating Data (Duration-1hr)
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
Module 7: Statistics Basics (Duration-11hrs)
- 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
Module 8: Machine Learning
Module 9: Supervised Learning (Duration-6hrs)
- Linear Regression
- Linear Equation
- Slope
- Intercept
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure
- ROC curve
- Bias Variance Tradeoff
Module 10: Unsupervised Learning (Duration-4hrs)
- K-Means
- K-Means ++
- Hierarchical Clustering
Module 11: Other Machine Learning algorithms (Duration-10hrs)
- K – Nearest Neighbour
- Naïve Bayes Classifier
- Decision Tree – CART
- Decision Tree – C50
- Random Forest
For More Details About Data Science with Python Course Click Here! →
Data Science with SAS Training Syllabus
Overview 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:
- 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
For More Details About Data Science with Python Course Click Here! →
Data Science with R Training Syllabus
Module 1: Introduction to Data Science (Duration-1hr)
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
Module 2: Introduction to R (Duration-1hr)
- What is R?
- Why R?
- Installing R
- R environment
- How to get help in R
- R Studio Overview
Module 3: R Basics (Duration-5hrs)
- Environment setup
- Data Types
- Variables Vectors
- Lists
- Matrix
- Array
- Factors
- Data Frames
- Loops
- Packages
- Functions
- In-Built Data sets
Module 4: R Packages (Duration-2hrs)
- DMwR
- Dplyr/plyr
- Caret
- Lubridate
- E1071
- Cluster/fpc
- Data.table
- Stats/utils
- Ggplot/ggplot2
- Glmnet
Module 5: Importing Data (Duration-1hr)
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
Module 6: Manipulating Data (Duration-1hr)
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
Module 7: Statistics Basics (Duration-11hrs)
- 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
- Scatter plot
- Cook’s Distance
- Missing Value treatments
- What is a NA?
- Central Imputation
- KNN imputation
- Dummification
- Correlation
- Pearson correlation
- Positive & Negative correlation
Module 8: Error Metrics (Duration-3hrs)
- Classification
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- Regression
- MSE
- RMSE
- MAPE
Module 9: Machine Learning
Module 10: Supervised Learning (Duration-6hrs)
- Linear Regression
- Linear Equation
- Slope
- Intercept
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure
- ROC curve
- Bias Variance Tradeoff
Module 11: Unsupervised Learning (Duration-4hrs)
- K-Means
- K-Means ++
- Hierarchical Clustering
Module 12: Machine Learning using R (Duration-10hrs)
- Linear Regression
- Logistic Regression
- K-Means
- K-Means++
- Hierarchical Clustering – Agglomerative
- CART
- 5.0
- Random forest
- Naïve Bayes
