Data Science Curriculum
1. Programming with Python.
  • Declaring, using variables, Data types.

  • Input/Output, Type conversion.

  • Conditional, Loop statements. List, Tuple, Set & Dictionary.

  • Creating/Using Functions & Classes.

  • Creating connection between databases.

                  <SQLite (SQL)>

                  <Mongo (No-SQL>

  • Exercise questions for more practice.

2. Data Analysis in Python
  • Importing & Reading your data

  • Getting & Knowing your data.

  • Filtering & Sorting of data

  • Grouping data

  • Working on Multi Index data.

  • Joins, Comparisons. Getting statistics from your data.

  • Exercise datasets for more practice.

3. Building Apps with state
  • Displaying data’s distribution

                <Histograms, Bar Charts>

                <Line Chart, Stacked Bar Chart>

  • Plotting correlations or heat map                             <Scattered Chart>                                             <Heat maps, Pair Plot, Facets>

  • Saving figures for Storytelling

  • Exercise datasets for more practice.

4. Machine Learning & Forecasting Techniques using Python
  • Performing classification techniques to find suitable classes of data for identifying best values.

                    <Logistic Regression>

                         <K Nearest Neighbors>

                         <Decision Tree>

                         <Support Vector Machine>

  • Performing Regression techniques to find suitable value.

                          <Linear Regression>                                          <Ridge & Lasso Regression>

                          <Decision Tree Regressor>

  • Ensemble Learning

                    <Bagging – Voting Classifier>

                    <Boosting – XGBoost Classifier>


  • Unsupervised Machine Learning


                     <Dimensionality Reduction>


  • Making the best recommendation system based on previous user’s data.                                  <Collaborative Filtering>

  • Performing Time Series forecasting in our data.


                   <Time Series visualization>

                   <Naïve Approach for forecasting>

                   <Moving Approach for forecasting

                    ARIMA model>

5. Deep Learning
  • Computer Vision

     <Image manipulation & Processing>

     <Image Segmentation & Object Detection>

  • TensorFlow & CNN

          <Activation Functions>

          <Understanding TensorBoard>

  • Natural Language Processing

            <Regular Expression>

            <Sentiment Analysis>

7. Creating, Viewing reports in Power BI
  • Creating power pivot in Excel.

  • Introduction to Power Query Editor.

  • Creating and managing relationships.

  • Understanding the use of measures.

  • Creating & Viewing reports.

  • Managing KPI’s

6. Data Analysis & Modeling in Excel
  • Understanding basic functionality of Excel.

  • Writing queries and performing conditions on the data.

  • Using inbuilt functions.

  • Data Validation.

  • Visualizing data using Charts in Excel.

  • Creating new data using Pivot Table