Datascience With Python Online Training

1)    Introduction to Data Science 

• Importance
• Prospects 
• Data science tools & technologies 
• What is Machine Learning? 
• Why Machine Learning in Data Science 

2)    Understanding Data 

• Types of data 
• Raw data handling 
• Processed or transformed data 
• Decision making from data 
• Statistics: Making sense of data 

3)    Statistics and Probability

• Uni Variate analysis 
• Measure of central tendency 
• Mean – Median – Mode 
• Range 
• IQR 
• Variance 
• Standard deviation 
• Correlation
• ANOVA 

4)    Applied Maths
5)    Probability Distributions 

• Skewness 
• Kurtosis 
• Gaussian distribution 
• Multivariate Gaussian distributions 
• Binomial distributions 
• Poisson distributions 

6)    Machine learning Overview 

• Supervised learning – regression, classification 
• Unsupervised learning - clustering 
• Reinforcement learning 

7)    Basics of python for data analysis 

• Basics of python for data analysis
• Datatypes in Python
• Working with different datatypes
• Important Packages in Python
• Anaconda
• Exploratory analysis in python using Pandas
• Data Munging in Python using Pandas

8)    Dictionary 

• Creating a Dictionary 
• Accessing Values in Dictionary 
• Updating Dictionary 
• Delete Dictionary Elements 
• Properties of Dictionary Keys 

9)    Tuples 

• Creating a Tuples 
• Accessing Values in Tuples: 
• Updating Tuples 
• Delete Tuple Elements 
• Basic Tuples Operations 

10)    List 

• Creating a list 
• Accessing Values in Lists 
• Updating Lists 
• Delete List Elements 

11)     Numpy
12)    Pandas
13)    Matplotlib
14)    Seaborn
15)    The Math and Stats Packages
16)    Scikit-learn
17)    Regression 

• Linear regression 
• Hypothesis 
• Gradient Descent 
• Prediction 
• Normalization 
• Hands on 
• Logistic regression 
• Sigmoid function 
• Decision Boundary 

18)    PCA
19)    AUC – ROC Curve
20)    Bias – Variance trade-off
21)    Overfitting and underfitting
22)    Classification evaluation matrics 
23)    Model evaluations 

• Mean Squared Error 
• K fold cross validation 
• Accuracy, Precision, Recall 

24)    Tree based models 

• What is a decision tree? 
• Decision tree algorithms 
• How does it work? 
•  Implementation 

25)    Ensemble methods of trees based models 

• Random forest 
• What is random forest? 
• Advantages of random forest 
• Disadvantages of random forest 
• Random forest implementation 

26)    K – Nearest Neighbor 

• What is KNN algorithm? 
• How to select appropriate k value? 
• Calculating distance 
• KNN algorithm – pros and cons 

27)    Cluster analysis 

• Why clustering? 
• K means clustering 
• Number of clusters k=? 
• Pros and cons 

28)    Support Vector Machines 

• Overview 
• Classification Using a Separating Hyperplane 
• The Maximal Margin Classifier _Non-separable Case 
• Support Vector Classifiers - Details 
• Support Vector Machines - Classification with non-linear boundaries 

29)    Time Series Analysis –ARIMA
30)    Pickling
31)    Data visualization 

• How to create a scatter plot? 
• How to create a histogram? 
• How to create a bar chart? 
• How to create a stacked bar chart? 
• How to create a box plot? 
• How to create an area chart? 
• How to create a heat map? 
• How to plot a geographical map? 

      32) Neural Network
      33)  Tensorflow
      34) Keras
      35) NLP

• Practice questions/case studies will be shared.
• Practice Project work will be shared towards the end of the session.
• Will share interview questions for preparation.