Example Curriculum

  Batch 118 - link, Passcode & update section
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  Induction, Software Installation
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  Introduction to Data Analytics, Python Basic programming
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  Getting started with excel, Operators in Python
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  Excel Pivot , Excel Project, Excel Assignment (Assignment - 1)
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  Operators in Python, Conditions in Python
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  Conditions and Loops in Python
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  Practice programs with Loops and conditions, Strings in Python - 1
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  Strings in Python
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  List, Tuple and Introduction to Dictionary
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  Dictionary, Sets and Functions - Part 1
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  1. Functions - part 2 2. On line functions / lambda 3. Map Filter Reduce in Lists
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  1. Class and Objects 2. Python Assignment (Assignment 2) 3. Database introduction (Database fundamentals session 1)
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  MYSQL Installation Creating Tables Foreign keys Importing data from CSV
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  1. INSERT INTO TABLE 2. ALTER TABLE 3. UPDATE query 4. SELECT (All version) 5. JOINS
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  1. Database last session 2. Descriptive statistics introduction
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  1. Descriptive statistics: a. Types of data b. measure of central tendency
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  1. Measure of Variability 2. How to analyze one data
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  1. Analysing 2 datasets : Categorical v Categorical and Numerical v Numerical
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  1. Inferential statistics 2. Probability Distribution 3. Introduction to Advanced Python for Data Analysis
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  Numpy
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  1. Linear Algebra using Numpy 2. Data analysis using Pandas
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  Data Preprocessing: Handling missing values
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  Data Preprocessing using Python
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  10th may
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  1. Data Analytics Projects - Projects to practice 2. Power - Data Transformation:
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  Build basic visuals using Power BI
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  Various basic visualizations and creating dashboardsrd
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  1. Data Modelling 2. Data Transformation
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  1. DAX in Power BI
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  1. Python script to load data in Power BI 2. Python script to perform preprocessing in Power BI 3. Visualization using columns from different tables (related) 4. Introduction to Tableau
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  Power BI Slicers Power BI Filters Row Level Security in Power BI Introduction to Report Builder
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  1. Visualization using Tableau 2. Data preprocessing using Natural Language Processing (NLP)
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  1. Advanced Excel 2. Reading data from the Database into Python 3. Reading content of a JSON file into Python
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  21st June
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  1. Data cleaning using Pandas 2. Web scraping 3. Project on Data Cleaning 4. Additional practice questions on data cleaning
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  28th June
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  1. Projects 2. Github 3. Doubt Discussion
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  1. Introduction to Machine Learning - Part 2 2. Data preprocessing: Missing values, Categorical values and Scalling
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  1. Data Cleaning for ML 2. ML Process 3. Simple Linear Regression
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  1. Multiple linear regression 2. Logistics regression 2a. Confusion matrix 3. ML projects
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  1. Visualize Classification Result 2. KMeans Clustering algorithm
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  1. Hierarchical clustering 2. Apriori algorithm under Association 3. Introduction to Time Series
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  Time Series Analysis Part 1
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  1. Time Series Analysis Part 2 2. Additional reading material for Machine Learning
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  11th August
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  12th August
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  13th August
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  14th August
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  15th August
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  16th August
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  1. Introduction to AI 2. Deep Learning 3. Introduction to Cloud Computing
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  1. NLP - Data pre-processing, Word cloud and Sentiment Analysis 2. Model Deployment using Flask
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  26th August
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  Resumes building and Sample Interview Questions
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  Further Placement Assistance Process
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  6th September
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