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Data Science with Python Content
Data Science with Python Content
Curriculum
15 Sections
28 Lessons
10 Weeks
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Introduction to Data Science, Machine Learning
1
2.1
Introduction to Data Science, Machine Learning
30 Minutes
Python
14
3.1
Python Syntax
30 Minutes
3.2
Python Collections or Sequence
30 Minutes
3.3
Python Functions
30 Minutes
3.4
Python Modules
30 Minutes
3.5
Python File handling
30 Minutes
3.6
Object Oriented Python
30 Minutes
3.7
Polymorphism
30 Minutes
3.8
Variables, Basic Operators, Decision Making, Loops
30 Minutes
3.9
Numbers, Strings, Lists, Tuples, Dictionary
30 Minutes
3.10
Python Libraries
30 Minutes
3.11
Introducing Data Frames
30 Minutes
3.12
Exceptions
30 Minutes
3.13
Python MySQL Database Access
30 Minutes
3.14
Introducing Data Frames
30 Minutes
Introduction to Descriptive Statistics
1
4.1
Introduction to Descriptive Statistics
30 Minutes
Summarizing data
1
5.1
Summarizing data
30 Minutes
. Introduction to Inferential Statistics
1
6.1
Introduction to Inferential Statistics
30 Minutes
Probability
1
7.1
Probability
30 Minutes
. Permutations and Combinations
1
8.1
Permutations and Combinations
30 Minutes
Combinatorics
1
9.1
Combinatorics
30 Minutes
Random Variables
1
10.1
Random Variables
30 Minutes
Central Limit Theorem
1
11.1
Central Limit Theorem
30 Minutes
Common Distributions
1
12.1
Common Distributions
30 Minutes
Skewness and Kurtosis
1
13.1
Skewness and Kurtosis
30 Minutes
Accuracy
1
14.1
Accuracy
30 Minutes
Machine Learning Algorithms
1
15.1
Machine Learning Algorithms
30 Minutes
Project Work
1
16.1
Project Work
30 Minutes
Machine Learning Algorithms
Supervised Algorithms
Unsupervised Algorithms
Train and Test Data
Linear Regression with Example
Regression Case Study with Activity
R – Square
Regression Coefficients
Logistic Regression with Example/Activity
Case Study with Activity
Cross Validation
Exploratory Analysis
K Nearest Neighbors with example
Support Vector Machines with example
Naïve Bayes Algorithm with Example
Decision Trees with Example
Information Gain
Data Impurity and Entropy
Ensemble Methods
Bagging
Boosting
Bias and Variance
Overfitting
Underfitting
Precision and Recall
Defining Accuracy
Gradient Descent
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