Data Science Curriculum
1. Programming with Python.
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Declaring, using variables, Data types.
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Input/Output, Type conversion.
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Conditional, Loop statements. List, Tuple, Set & Dictionary.
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Creating/Using Functions & Classes.
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Creating connection between databases.
<SQLite (SQL)>
<Mongo (No-SQL>
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Exercise questions for more practice.
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2. Data Analysis in Python
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Importing & Reading your data
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Getting & Knowing your data.
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Filtering & Sorting of data
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Grouping data
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Working on Multi Index data.
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Joins, Comparisons. Getting statistics from your data.
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Exercise datasets for more practice.
3. Building Apps with state
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Displaying data’s distribution
<Histograms, Bar Charts>
<Line Chart, Stacked Bar Chart>
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Plotting correlations or heat map <Scattered Chart> <Heat maps, Pair Plot, Facets>
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Saving figures for Storytelling
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Exercise datasets for more practice.
4. Machine Learning & Forecasting Techniques using Python
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Performing classification techniques to find suitable classes of data for identifying best values.
<Logistic Regression>
<K Nearest Neighbors>
<Decision Tree>
<Support Vector Machine>
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Performing Regression techniques to find suitable value.
<Linear Regression> <Ridge & Lasso Regression>
<Decision Tree Regressor>
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Ensemble Learning
<Bagging – Voting Classifier>
<Boosting – XGBoost Classifier>
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Unsupervised Machine Learning
<Clustering>
<Dimensionality Reduction>
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Making the best recommendation system based on previous user’s data. <Collaborative Filtering>
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Performing Time Series forecasting in our data.
<Fundamentals>
<Time Series visualization>
<Naïve Approach for forecasting>
<Moving Approach for forecasting
ARIMA model>
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5. Deep Learning
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Computer Vision
<Image manipulation & Processing>
<Image Segmentation & Object Detection>
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TensorFlow & CNN
<Activation Functions>
<Understanding TensorBoard>
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Natural Language Processing
<Regular Expression>
<Sentiment Analysis>
7. Creating, Viewing reports in Power BI
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Creating power pivot in Excel.
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Introduction to Power Query Editor.
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Creating and managing relationships.
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Understanding the use of measures.
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Creating & Viewing reports.
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Managing KPI’s
6. Data Analysis & Modeling in Excel
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Understanding basic functionality of Excel.
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Writing queries and performing conditions on the data.
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Using inbuilt functions.
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Data Validation.
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Visualizing data using Charts in Excel.
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Creating new data using Pivot Table