Duration: Hours

Python, which once was considered a general programming language, has emerged as a shining star of the Data Science world. The key driver is the flexibility it offers for an end-to-end enterprise-wide analytics implementation, including machine learning and AI. No wonder Python for data science has become the industry’s preferred choice; hence investing in a comprehensive data science python course becomes important for any aspirant.

## Description

### a). Introduction to Basic Statistics

1. Introduction to Statistics

2. Measures of central tendencies

3. Measures of variance

4. Measures of frequency

5. Measures of Rank

6. Basics of Probability, distributions

7. Conditional Probability (Bayes Theorem)

### b). Introduction to Analytics & Data Science

1. What are analytics & Data Science?

2. Business Analytics vs. Data Analytics vs. Data Science

3. Common Terms in Analytics

4. Analytics vs. Data warehousing, OLAP, MIS Reporting

5. Types of data (Structured vs. Unstructured vs. Semi Structured)

6. Relevance of Analytics in industry and need of the hour

7. Critical success drivers

8. Overview of analytics tools & their popularity

9. Analytics Methodology & problem-solving framework

10. Stages of Analytics

### c). Introduction to Mathematical Foundations

1. Introduction to Linear Algebra

2. Matrices Operations

3. Introduction to Calculus

4. Derivatives & Integration

5. Maxima, minima

6. Area under the curve

7. Theory of optimization

### d). Visualising Geospatial data

1. Introduction to Folium

2. Maps with Markers

3. Choropleth Maps

### e). Operations with NumPy (Numerical Python)

1. What is NumPy?

2. Overview of functions & methods in NumPy

3. Data structures in NumPy

4. Creating arrays and initializing

5. Reading arrays from files

6. Special initializing functions

7. Slicing and indexing

8. Reshaping arrays

9. Combining arrays

10. NumPy Maths

### f). Python Essentials (Core)

1. Overview of Python- Starting with Python

2. Why Python for data science?

3. Anaconda vs. python

4. Introduction to installation of Python

5. Introduction to Python IDE’s (Jupyter, /Ipython)

6. Concept of Packages – Important packages

7. NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc

8. Installing & loading Packages & Name Spaces

9. Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)

10. List and Dictionary Comprehensions

11. Variable & Value Labels – Date & Time Values

12. Basic Operations – Mathematical/string/date

13. Control flow & conditional statements

14. Debugging & Code profiling

15. Python Built-in Functions (Text, numeric, date, utility functions)

16. User defined functions – Lambda functions

17. Concept of apply functions

18. Python – Objects – OOPs concepts

19. How to create & call class and modules?

### g). Overview of Pandas

1. What are pandas, its functions & methods

2. Pandas Data Structures (Series & Data Frames)

3. Creating Data Structures (Data import – reading into pandas)

### h). Cleansing Data with Python

1. Understand the data

2. Sub Setting / Filtering / Slicing Data

3. Using [] brackets

4. Using indexing or referring with column names/rows

5. Using functions

6. Dropping rows & columns

7. Mutation of table (Adding/deleting columns)

8. Binning data (Binning numerical variables into categorical variables)

9. Renaming columns or rows

10. Sorting (by data/values, index) -By one column or multiple columns – Ascending or Descending

11.Type conversions

12. Setting index

13. Handling duplicates /missing/Outliers

14. Creating dummies from categorical data (using get dummies ())

15. Applying functions to all the variables in a data frame (broadcasting)

16. Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc.)

### i). Data Analysis using Python

1. Exploratory data analysis

2. Descriptive statistics, Frequency Tables, and summarization

3. Uni-variate Analysis (Distribution of data & Graphical Analysis)

4. Bi-Variate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)

### j). Data Visualization with Python

1. Introduction to Data Visualization

2. Introduction to Matplotlib

3. Basic Plotting with Matplotlib

4. Line Plots

### k). Visualisation Tools

1. Basic Visualization Tools

2. Area Plots

3. Histograms

4. Bar Charts

5. Pie Charts

6. Box Plots

7. Scatter Plots

8. Bubble Plots

9. Advanced Visualization Tools

10. Waffle Charts

11. Word Clouds

12. Seaborn and Regression Plots

### l). Statistical Methods & Hypothesis Testing

1. Descriptive vs. Inferential Statistics

2. What is probability distribution?

3. Important distributions (discrete & continuous distributions)

4. Deep dive of normal Distributions and Properties

5. Concept of sampling & types of sampling

6. Concept of standard error and central limit theorem

7. Concept of Hypothesis Testing

8. Statistical Methods – Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square.

For more inputs on Python for DataScience you can connect here.
Contact the L&D Specialist at Locus IT.

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