Data Engineering Certification Course

Module 2

1. A-Z Python for data science Installation

of python IDE’S

2. Python environment Setup.
3. Python data types: List, Tuple Set,

Dictionary

4. Conditional statements
5. If
6. If-else.
7. Nested if
8. if elif ladder
9. Loops
10. for loop,
11. while loop
12. Functions
13. Custom functions
14. Inbuilt function.
15. OOP Concept
16. Class & object
17. Inheritance
18. Init method
19. Exception handling
20. Try
21. Except
22. Finally
23. Types of exceptions
24. File handling
25. Read
26. Write
27. Append

Module 3 – Statistics

1. Definition
2. Inferential and descriptive statistics
3. Mean, median, mode
4. Variant, standard deviation, range,

Skewness, kurtosis, untitance and

Interval, z-distribution, tardancy,

p-value, f-test, anova, chi-square test and

masseuse of dispersion

Module 4 – Probability

1. Definition, types of probability

(conditional probability, joint probability)

2. Random variables, probability

distribution, bayer’s theorem

3. Linear algebra, eigen vectors & eigen

values, maximize & minimize functions.

Iift ratio, orthogonal matrix

4. Central limit theorem, hypothesis

testing, power law

5. Correlation regression & covariance
6. Probability mass function, cumulative

distribution function.

Module 5 – Numpy

1. Installation & introduction of NumPy

package

2. NumPy basics
3. Creation of NumPy arrays
4. Array operations
5. Array slicing
6. Multidimensional array
7. Python list VS NumPy arrays
8. Basic linear algebra operations

Module 6 – Pandas

1. Installation and introduction of pandas

package

www.datasparklearning.com

2. Pandas basics
3. Indexing and Recording files
4. Data operations
5. Grouping, merging, joining &

concatenating

6. Creating objects
7. Viewing data
8. Data selection
9. Data manipulation
10. Working with data & time
11. Working with different types of files

(CSV, Excel, Text file etc.)

Module 7 – Data Visualisation

1. Basics of visualisation
2. Installation of visualisation packages

like matplotlib, seaborn etc.

3. Working with different types of plot /

grapes

1. Scatter
2. Line chart
3. Bar chart
4. Histogram
5. Boxplot
6. a-a plot
7. Pie-chart etc

Module 8 – Data preparation / data cleaning

/ munging / wrangling

1. Outlier analysis / treatment
2. Missing value imputation
3. Data filtering
4. Typecasting
5. Transformations
6. Duplicate data handling
7. Categorical data handling
8. Discretization
9. Standardisation and normalisation of data
10. Zero & near zero variance features

Module 9 – Feature Engineering

1. Rounding
2. Binarization
3. Binning
4. Transformations
5. Feature engineering on text data
6. Feature scaling
7. Feature selection techniques

Module 1:-Basics

• Database Concepts
• E-R Modeling and Diagram
• Normalization
• SQL Server
• Introduction to SQL
• DDL and DML Statements
Module 2: Queries (DQL)
• Select Statement
• Top, Distinct, Null etc…Keywords
• String and Arithmetic Expressions
• Where Clause with Operators
• Sorting data using Order By clause, basic of Sub Queries
Module 3: Aggregate Functions
• functions in Queries
• predefined functions
• Group By with Rollup and Cube and Group By with Rollup and Cube
• Count, Sum, Min, Max, Avg Group By and Having Clause
Module 9: Joins and Set – Operations
• Introduction to Joins Cross Joins
• Inner Join, Outer Join, Self-Join
• Unions, Intersect and Except
• Implementation of Data integrity
Module 5: Constraints
• Unique
• Not NULL
• Primary Key
• Default Check Foreign Key
Module 6: Implementing Views
• Introduction & Advantages of Views
• Creating, Altering, Dropping Views, SQL Server Catalogue Views
Module 7: Extra – Features
• Pivot Table
• Common Table Expression
• Ranking Functions Using BLOB data type
• Using XML data type