COURSE DETAILS

Programming in Python

What does this course include?

• Python
• R
• Tableau
• Structured Query Language(SQL)
• Advanced Excel

COURSE CONTENT

• Section #1: Fundamentals of Python
  • Basics of Python
  • Conditional and loops
  • String and list objects.
  • Functions & OOPs concepts.
  • Exception handling
  • Database programming.
• Section #2: Data Wrangling
  • Reading CSV, JSON, XML and HTML files using Python
  • NumPy & Pandas
  • Relational databases and data manipulation with SQL
  • Loading, cleaning, transforming, merging, and reshaping data
• Section #3: Statistics and Probabality
  • Probability mass functions
  • Probability distribution functions
  • Cumulative distribution functions
  • Modeling distributions
  • Inferential statistics
  • Estimation
  • Hypothesis testing
  • Implementation of statistical concepts in Python

Project Work To Be Done by Learners

• Section #1: Machine Learning Model in Python
  • Building models using below algorithms
  • Linear and logistics regression
  • Decision trees
  • Support vector machines (SVMs)
  • Random forests
  • XGBoost
  • K nearest neighbour & hierarchical clustering
  • Principal component analysis
  • Text analytics and time series forecasting
• Section #2: Data Visualization Using Matpolib and Tableau
  • Interactive visualizations with Matplotlib
  • Data visualizations using Tableau
  • Tableau dashboard and story board
  • Tableau and R integration
• Section #3: Deep Learning Using Tensor Flow
  • Basics of neural networ
  • Linear algebra
  • Implementation of neural network in Vanilla
  • Basics of TensorFlow
  • Convolutional neural networks (CNNs)
  • Recurrent neural networks (RNNs)
  • Generative models
  • Semi-supervised learning using GAN
  • Seq-to-seq model
  • Encoder and decoder
• Section #4: Handling Big Data With Spark
  • Introduction to Big Data & Spark
  • RDD's in Spark, data frames & Spark SQL
  • Spark streaming, MLib & GraphX

R Language

• Section #1: Programming in R
  • Basics of R
  • Conditional and loops
  • R packages/libraries
  • Data mining GUI in R
  • Data structures in R
  • Exceptions/ debugging in R
• Section #2: Data wrangling
  • Reading CSV, JSON, XML, .XLSX and HTML files using R
  • ETL operations in R
  • Sorting/ merging data in R
  • Cleaning data
  • Data management using dplyr in R
• Section #3: Statistics and Probability
  • Descriptive statistics, random variables, and probability distribution functions
  • Data distributions like uniform, binomial, exponential, poisson, etc
  • Probability concepts, set theory and hypothesis testing
  • Central limit theorem, t-test, chi-square, z-test
  • Central limit theorem
  • ANOVA
• Section #4:Modeling in to R
  • Linear regression model in R
  • Multiple linear regressions model
  • Representation of regression results
  • Non-linear regression models
  • Tree-based regression models
  • Decision tree-based models
  • Rule-based systems
• Section #5:Mining Algorithms using R
  • Association analysis
  • Market-based analysis/ rules
  • Apriori algorithm
  • Ensemble models - random forest model, boosting model
  • Segmentation analysis- types of segmentation, k-means clustering, Bayesian clustering.
  • Feature selection/ dimension reduction- multidimensional scaling, dimension reduction, factor or component analysis
  • Axes
  • Covariance
• Section #6: Time series forecasting in R and Model Deployment
  • Basics of time series
  • Components of time series
  • Time series forecasting
  • Deploying predictive models
  • Using SQL server
  • Using external tools
  • Using big data tools
  • Integrating R with Hadoop/Spark
• Section #7: Data wrangling using R and SQL
  • SQL queries
  • Integrating with R
  • Deployment and execution
  • Data Modeling and Formatting using Excel
  • Excel formulas to perform analytics
  • Macros for job automation
• Section #8: Data Analysis and Visualization using Tablue
  • Introduction to Tableau and its layout
  • Connecting tableau to files and databases
  • Data filters in Tableau
  • Calculation and parameters
  • Tableau graphs and maps
  • Creating Tableau dashboard
  • Data blending
  • Creating superimposed graphs
  • Integrating Tableau with R