Curriculum
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High level overview of data science, business intelligence, analytics and data engineering with real world examples
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Statistics overview and descriptive statistics.
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Moments of Statistics, measures of spread, shape and errors of data.
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1. Function & it's type 2. Lambda Function 3. Module-Math ,Random 4. Exceptional Error handling
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1.Pandas Library --Series, Dataframe, Basics of Statistics
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Basics of Programming and python
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Python data structures, functions
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Basic concepts of pandas and numpy along with their functions
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Basics of Excel, Excel functions and Pivot Table
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Advanced excel and statistics using excel
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Databases, SQL, SQL syntax and logic building
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Hands on practice of SQL and introduction to data visualization
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Data Visualization and Power BI - Introduction to data visualization and Power BI feature
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Report Visualization and properties - Hands on practice Report Visualization
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Hierarchies and drill down reports - Hierarchies and Drill down reports in Power BI
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DAX expressions - DAX expressions in power bi
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PowerBI deployment and cloud usage
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Data Wrangling, data cleaning and EDA
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Statistical analysis using visualization in Python
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Supervised Machine Learning and Linear Regression
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Theoretical Explanation of Linear regression
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Explanation of Logistic Regression
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KNN, Decision Trees
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Decision Trees, Random Forest and KNN
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Practical: Decision Trees, Random Forest and KNN
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K Means Algorithm, Hierarchal Clustering
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Practical and theory: NLP
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Basics of Deep Learning, Intro to AI and Cloud Computing
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Artificial Neural Network
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ANN, Optimizers and Cost functions
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Practice module for Deep Learning with Keras
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Mask Detection project
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Object detection using Yolo, OpenCV
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Multiple object Detection using Yolo, OpenCV, Intro to Google Colab
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Introduction to cloud computing
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New section
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29th March Recording
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