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Courses

Data Analytics

  1. Conditional Formatting
  2. Conditional Formatting
  3. Text Functions
  4. Conditional Formatting
  5. Logocal Functions If, and or
  6. Lookup functions : Vlookup, hlookup
  7. Index and Match function
  8. Date and Time Functions
  9. Arithmatic functions
  10. Statistical Functions
  11. Sort and Filter
  12. What if Analysis
  1. How to create Pivot Table
  2. Aggregtaion using Pivot tables
  3. Custom Aggregation in Pivot Table
  4. Creating a Calculated Field
  5. Using Slicer
  6. Pivot table for Dashboarding
  1. Cretaing Simple Charts
  2. Creating a Combination Chart
  3. Chart Styles and Formatting
  4. Thermomenter Chart, Gant Chart Waterfall Chart
  5. Pivot Charts
  6. Interactive Charts for Dashboarding
  1. What is SQL and SQL DataBase SQL Syntax
  2. SQL : Create Table
  3. SQL : Select and Select Distinct
  4. SQL : Insert Update and delete functions
  5. SQL : Group by, Order By, Having and Where statements
  6. SQL : Statistical Functions – Min , Max, Sum, Avg, first, last

Python Basics

Rs. 25000

Data Sciences

  1. Overview & History
  2. IDEs of R
  3. R vs Python
  4. Why R for analytics
  5. R Variables
  6. R Operators
  7. R objects – dataframe
  8. Subsetting / Indexing : vectors , matrix dataframes list
  9. Missing Values
  10. Vectorised Operations
  1. Conditional Statements in R
  2. Loops in R : for loop, while loop and repeat loop
  3. Functions in R
  4. R Packages
  5. Built in Functions in R
  6. Apply Family functions
  7. Working with Dates in R
  8. R Markdown
  1. Data Wrangling
  2. Reading Data in R
  3. Subsetting Dataframes
  4. Creating New Variables
  5. Sorting Data
  6. Data Summarising
  7. Merging Data tables
  8. Dplyr package for data manipulation
  1. 1 Need of Data Visualization
  2. Creating plots in R
    1. 1 Bar Chart
    2. Pie Chart
    3. Histogram
    4. Kernel Density Plot
    5. Box and Whisker Plot
    6. Scatter plot
    7. Line Chart
    8. Heat Map
    9. Word Cloud
  3. ggplot2 for plotting
  4. File format and graphic output
  5. Introduction to R Shiny for data visualization
  1. Hypothesis testing
  2. Null and Alternate Hypothesis
  3. Type 1 and Type 2 errors
  4. Statistical significance
  5. Hypothesis test procedure
  6. p value in a hypotheis test
  7. one sample hypothesis test
  8. two sample hypothesis test
  9. ANOVA
  10. Non Paramteric Tests
  1. Correlation
  2. Regression analysis
  3. Evaluation metrics for Regression : R squared adjusted R squared MSE RMSE
  4. Assumptions of Regression
  5. Multicollinearity
  6. Non Linear Regression
  7. Working with Categorical data
  8. Validation Framework
  1. Introduction to Classification
  2. Logistic Regression
  3. k-nearest neighbors(KNN)
  4. Decision Trees
  5. Random Forest
  6. Support Vector Model
  7. Naïve Bayes
  8. Model Evaluation
  1. Introduction to Clustering
  2. Clustering Vs Classification
  3. Clustering Methods
  4. K Means Clustering
  5. Hierarchical Clustering
  6. DBSCAN Clustering
  7. Principal Component Analysis
  1. Introduction to Association Mining
  2. Apriori Algorithm
  3. Apriori Basic Concepts
  4. Apriori Working
  5. Apriori in R
  6. Applications of Association Rules

Rs. 68000

Machine Learning

  1.  Introduction to Machine Learning
  2. Applications of Machine Learning
  3. Types of Machine Learning
  4. Python essentials for Machine Learning
  1.  Data Wrangling using Pandas and NumPy
  2. Data Summarizing
  3. Univariate and Multivariate Plots
  4. Handling Missing Values
  5. Dealing with Categorical data
  6. Scaling of data
  1. Linear Regression
  2. Cost Function
  3. Gradient Descent
  4. Regularization of Linear Regression
  5. Logistic Regression
  1. K Nearest Neighbour Classifiers
  2. Decision Tree Regressors/ Classifiers
  3. Naïve Bayes Classifier
  4. Support Vector Machine Classifiers
  1. Bootstrap Aggregation – Bagging Algorithm
  2. Gradient Boosting
  3. Random Forest
  1. Principal Component Analysis – Unsupervised Dimensionality Reduction
  2. Linear Discriminant Analysis – Supervised Dimensionality Reduction
  1. Linkage Based Clustering – Hierarchical Clustering
  2. K– Means Clustering
  3. Spectral Clustering
  1. What is recommender system
  2. Prediction and Ranking Proble
  3. Content Based Recommendation Systems
  4. Collaborative Filters
  5. Hybrid Recommenders
  1. Time Series: basic visualization and summary
  2. Stationary Process , MA and AR Process
  3. ARMA Models
  4. Non Stationary Time Series Analysis : ARIMA models
  1. NLTK Package
  2. Tokenization
  3. POS Tagging
  4. Stemming and Lemmatization
  5. Bag of Words, N Gram
  6. Term Frequency – Inverse Document Frequency
  7. Basic Text Classification Model
  1. Introduction to Artificial Neural Network
  2. Deep Neural Network using Python
  3. Optimization and Tuning of Deep Neural Network Models
  4. Convolutional Neural Network
  5. Recurrent Neural Networks:
  6. Auto Encoders
  7. SGD and Backpropagation
  8. Reinforcement Learning

Rs. 75000