Data Science Masterclass
Data Science is a method of huge data analysis that automates analytical model building. It is a branch of artificial intelligence. A lot of opportunities are out there in the field of ml and AI in India. People already using ml in the field of image processing, pattern analysis, marketing, data analysis have a pretty good future in India. The Machine Learning sessions provide the participants technical training on the concepts and Machine Learning algorithms to develop the code. Participants will also learn to use different Python libraries. Instruction cum aided with live projects which will allow students to grasp concepts of the complete data science development life-cycle.
Learning Path
The learning path of any learning concept is rather very important, as it is the way through which the learning process is enabled. This data Science Master class is designed to make the participant learn from scratch and master the concepts with ease.
- Python for Data Science and advance ProgrammingÂ
- Libraries – Pandas, Numpy, Matplotlib, Seaborn, and Sklearn
- Python –Â Data Aggregation, Tokenization, Visualization, Statistical Analysis.
- Tools – Tableau and Power -Bi.
- Industry Projects – House Price Prediction, Customer Segmentation, Spam Classification, and More.
Program Overview
Day -1:Â Python for Data
Science (5 Solved end-to-end Data Science Projects in Python)
Day -2:Â Advanced Python Programming
Day -3: Pandas Library – Introduction
Day -4: Pandas Library – Data Structures
Day -5: Numpy library – Array Operations | Mathematical Functions
Day -6: Numpy – Sort, Search, and Counting Functions
Day -7:Â Â Matplotlib, Histogram Using Matplotlib | I/O With Numpy
Day -8: Matplotlib Library – Introduction, Pyplot API | Types Of Plots
Day -9: Seaborn Library
Day -10:Â SKLearn Library
Day -11:Â Google Colab Notebook
Day -12: Python – Date and Time, Data Wrangling
Day -13: Python – Data Aggregation
Day -14: Python – Word Tokenization, Stemming, and Lemmatization
Day -15: Python – Data Visualization
Day -16: Python – Statistical Analysis
Day -17: Python – Types Of Distribution
Day -18: Python – Correlation, Chi-Square Test , Linear Regression
Day -19: Tableau – Introduction and Tools
Day -20: Tableau – Data Sources , Worksheets
Day -21:Â Spatial Data Science For Covid-19 Disease Prediction
Day -22:  Power-BI – Introduction, Installation Steps, and Architecture
Day -23: Power-BI – Data Modelling, Visualization Options | Excel Integration
Day -24: Parkinson’s Disease Prediction – XG Boost Classifier
Day -25:Â House Price Prediction using Random Forest Regression
Day -26: Customer Segmentation Using ML – K-Means Clustering
Day -27:Â Home Loan Prediction using Decision Tree Classifier
Day -28:Â Spam Classification using NLP
Day -29:Â Hand-Written Digit Recognition Using CNN
Day -30:Â Churn Prediction using Deep Learning
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Orientation - Data Science Master Class
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Day -1:Â Python for Data Science (5 Solved end-to-end Data Science Projects in Python)
-
Day -2:Â Advanced Python Programming
-
Day -3: Pandas Library – Introduction
-
Day -4: Pandas Library – Data Structures
-
Day -5: Numpy library – Array Operations | Mathematical Functions
-
Day -6: Numpy – Sort, Search, and Counting Functions
-
Day -7:Â Â Matplotlib, Histogram Using Matplotlib | I/O With Numpy
-
Day -8: Matplotlib Library – Introduction, Pyplot API | Types Of Plots
-
Day -9: Seaborn Library
-
Day -10:Â SKLearn Library
-
Day -11:Â Google Colab Notebook
-
Day -12: Python – Date and Time, Data Wrangling
-
Day -13: Python – Data Aggregation
-
Day -14: Python – Word Tokenization, Stemming, and Lemmatization
-
Day -15: Python – Data Visualization
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Day -16: Python – Statistical Analysis
-
Day -17: Python – Types Of Distribution
-
Day -18: Python – Correlation, Chi-Square Test , Linear Regression
-
Day -19: Tableau – Introduction and Tools
-
Day -20: Tableau – Data Sources , Worksheets
-
Day -21:Â Spatial Data Science For Covid-19 Disease Prediction
-
Day -22:  Power-BI – Introduction, Installation Steps, and Architecture
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Day -23: Power-BI – Data Modelling, Visualization Options | Excel Integration
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Day -24: Parkinson’s Disease Prediction – XG Boost Classifier
-
Day -25:Â House Price Prediction using Random Forest Regression
-
Day -26: Customer Segmentation Using ML – K-Means Clustering
-
Day -27:Â Home Loan Prediction using Decision Tree Classifier
-
Day -28:Â Spam Classification using NLP
-
Day -29:Â Hand-Written Digit Recognition Using CNN
-
Day -30:Â Churn Prediction using Deep Learning
Reviews
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THANKS MADAM FOR NICE TEACHING AND LEARNING PROCESS
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Machine
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Really good flatform for Data science
THANKS MADAM FOR NICE TEACHING AND LEARNING PROCESS
IF I HAVE A PERSONAL DOUBT HOW CAN I CONTACT YOU MADAM
AS IN CHATBOX IT IS VERY FAST MOVING
Ok
First comment
Very much informative this course would like to 5ake place to this live session
Your blog helped me to improve myself in many ways thanks for sharing this kind of wonderful informative blogs in live. I have bookmarked more article from this website. Such a nice blog you are providing !
Good platform to learn the course.
Thanks madam for nice teaching
And thanks to pantech e learning
THANKS MADAM FOR NICE TEACHING AND LEARNING PROCESS
Thank you for the great sessions.