This course will prepare you to:

• Explain the architecture of the Datascience Certification component
• Configure and use new functionalities in Datascience Certification
• Use the standard Datascience Certification Sub Modules.
• Explain the Datascience Certification Controlling Configuration and Customization option.

• Introduction to Data Science
• Introduction to Python
• Basic Operations in Python
• Variable Assignment
• Functions: in-built functions, user defined functions
• Condition: if, if-else, nested if-else, else-if

• List: Different Data Types in a List, List in a List
• Operations on a list: Slicing, Splicing, Sub-setting
• Condition(true/false) on a List
• Applying functions on a List
• Dictionary: Index, Value
• Operation on a Dictionary: Slicing, Splicing, Sub-setting
• Condition(true/false) on a Dictionary
• Applying functions on a Dictionary
• Numpy Array: Data Types in an Array, Dimensions of an Array
• Operations on Array: Slicing, Splicing, Sub-setting
• Conditional(T/F) on an Array
• Loops: For, While
• Shorthand for For
• Conditions in shorthand for For

• Statistics & Plotting
• Seabourn & Matplotlib - Introduction
• Univariate Analysis on a Data
• Plot the Data - Histogram plot
• Find the distribution
• Find mean, median and mode of the Data
• Take multiple data with same mean but different sd, same mean and sd but different kurtosis: find mean, sd, plot
• Multiple data with different distributions
• Bootstrapping and sub-setting
• Making samples from the Data
• Making stratified samples - covered in bivariate analysis
• Find the mean of sample
• Central limit theorem
• Plotting
• Hypothesis testing + DOE
• Bivariate analysis
• Correlation
• Scatter plots
• Making stratified samples
• Categorical variables
• Class variable

• File I/O
• Series: Data Types in series, Index
• Data Frame
• Series to Data Frame
• Re-indexing
• Operations on Data Frame: Slicing, Splicing (also Alternate), Sub-setting
• Pandas
• Stat operations on Data Frame
• Reading from different sources
• Missing data treatment
• Merge, join
• Options for look and feel of data frame
• Writing to file
• db operations

• Data Aggregation, Filtering and Transforming
• Lamda Functions
• Apply, Group-by
• Map, Filter and Reduce
• Visualization
• Matplotlib, pyplot
• Seaborn
• Scatter plot, histogram, density, heat-map, bar charts

• Regression - Introduction
• Linear Regression: Lasso, Ridge
• Variable Selection
• Forward & Backward Regression
• Logistic Regression
• Logistic Regression: Lasso, Ridge
• Naive Bayes

• Unsupervised Learning - Introduction
• Distance Concepts
• Classification
• k nearest
• Clustering
• k means
• Multidimensional Scaling
• PCA

• Decision trees
• Cart C4.5
• Random Forest
• Boosted Trees
• Gradient Boosting

• SVM - Introduction
• Hyper-plane
• Hyper-plane to segregate to classes
• Gamma

Practice Test and Interview Questions