Data Science Intermediate Bootcamp, Chennai
Binary Chefs
WHO CAN APPLY?
Anyone who is inquisitive about Data, ready to put in the challenge of mastering multiple intersecting disciplines such as Analytics, Statistics, Mathematics, Business domain knowledge. It takes a lot to become a Data Scientist.
PRE-REQUISITES
Knowledge of math’s and statistics until the 12th grade level. Past coding experience is ideal.
Note: If you do not know coding, we will teach you the basics to prepare you for the Bootcamp program.
Note: If you do not know coding, we will teach you the basics to prepare you for the Bootcamp program.
Course Content
Introduction to Python
Fundamental Statistics
Modelling and Forecasting
Databases
Power BI
Introduction to Python
Data Types and Operators
Control Flow
Functions
Scripting
Data Types and Operators
Control Flow
Functions
Scripting
Fundamental Statistics
Visualizing relationships in data
Seeing relationships in data.
Making predictions based on data.
Simpson's paradox.
Probability
Introduction to Probability.
Bayes Rule.
Correlation vs. Causation.
Estimation
Maximum Likelihood Estimation.
Mean, Median, Mode.
Standard Deviation and Variance.
Outliers and Normal Distribution
Outliers, Quartiles.
Binomial Distribution.
Manipulating Normal Distribution.
Inference
Confidence Intervals.
Hypothesis Testing.
Regression
Linear regression.
Correlation.
Visualizing relationships in data
Seeing relationships in data.
Making predictions based on data.
Simpson's paradox.
Probability
Introduction to Probability.
Bayes Rule.
Correlation vs. Causation.
Estimation
Maximum Likelihood Estimation.
Mean, Median, Mode.
Standard Deviation and Variance.
Outliers and Normal Distribution
Outliers, Quartiles.
Binomial Distribution.
Manipulating Normal Distribution.
Inference
Confidence Intervals.
Hypothesis Testing.
Regression
Linear regression.
Correlation.
Main theme:
Probabilistic versus non-probabilistic modeling and supervised versus unsupervised learning
o classification
o regression
o clustering methods
o sequential models
o matrix factorization
o topic modeling and model selection
o Time series
Probabilistic versus non-probabilistic modeling and supervised versus unsupervised learning
o classification
o regression
o clustering methods
o sequential models
o matrix factorization
o topic modeling and model selection
o Time series
MySQL
Understanding Relational Databases
Queries to Extract Data from Single Tables
Queries to Summarize Groups of Data from Multiple Tables
Queries to Address More Detailed Business Questions
Understanding Relational Databases
Queries to Extract Data from Single Tables
Queries to Summarize Groups of Data from Multiple Tables
Queries to Address More Detailed Business Questions
Microsoft Power BI
• Getting data from various data sources
• Data Modeling
• Visualizations
• Publishing and sharing
• Introduction to DAX
• Getting data from various data sources
• Data Modeling
• Visualizations
• Publishing and sharing
• Introduction to DAX
WRITE TO US!
We will revert within 24 Hours!