VSS Innovative Technologies
Data Science Course( 69 Hrs to 86 Hrs )
1. Statistics( 19 Hrs - 28 Hrs )
Sr. No Topic Topic Hours
1 Introduction of Statistics-0.5hrs
2 Functions of Statistics 1
3 Types of Variables 0.5
4 Level of Measurement 1
5 Graphical Representation of Data : Histogram, Box plot, Bar Chart, Pie Chart, Frequency,Density-2 - 3hrs
6 Measuring Central Tendency - Mean,Median, Mode,Skewness3 - 4hrs
7 Measuring Spread -Power Range,Percentile, Variance,Standard Deviation3-5hr
8 Probability - MutualExclusive,Independent Eventand Dependent Event 2 - 3hr
9 Conditional Probability-Bayes Theorem-2hrs
10 Random Variable -Probability, Distribution,Cumulative,Distribution Function,
Probability Mass Function 1 - 2hrs
11 Sampling & Population 1 - 2hrs
12 Normal Distribution 1 - 2hrs
13 Hypothesis Testing -Type 1 Error, Type 2 Error 1 - 2hrs
2. Python Basics( 12 Hrs - 18 Hrs )
Sr. No Topics Topic Hours
1 Data Types - Primitive & Non - Primitive 5 - 6 Hrs
2 Functions 2 - 3 Hrs
3 Numpy
a) Array Creation
b) Dot Product 1 - 2 Hrs
4 Pandas - Dealing with CSV 1 - 2 Hrs
5 Matplotlib - Graph Plotting 1 - 2 Hrs
6 File Operation 2 - 3 Hrs
3. Machine Learning Content( 38 Hrs - 40 Hrs )
Sr. No Topic Topic Hours
1 Introduction to Machine Learning 1hrs
2 Naive Bayes 2hrs
3 KNN( K - Nearest Neighbor )2hrs
4 Logistic Regression 2hrs
5 Linear Regression 2
6 Multiple Linear Regression 1hrs
7 Decision Tree 2 - 3hrs
8 Random Forest 2 hrs
9 K - Means Clustering 2hrs
10 Confusion Matrix 2 hrs
11 Random Forest 2 hrs
12 Ensemble Method 2 hrs
13 SVM( Support Vector Machine ) 1hrs
14 Hierarchical Clustering 2hrs
15 PCA( Principle Component Analysis )1hrs
16 Introduction to Neural Networks 1hrs
17 Artificial Neural Networks 3hrs
18 Convolutional Neural Networks 3hrs
19 Recurrent Neural Networks 1hrs
20 Mini Project 4 - 5 Hrs