**Title**

StepLeaf’s Python Certification Training for DataScience Course is a kickstart to learn zero knowledge python programming and write down Python Scripts. This course gives in-depth understanding of data structures, Python programming fundamentals and working with data in Python

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Jul 25 | Sat,Sun (10.5 Weeks) Weekend Batch | 01:30 AM 03:30 AM |

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StepLeaf’s Python Certification Training for DataScience Course helps you to perform hands-on analysis using Python from Scratch. The data analysis is done in a Jupyter-based lab environment. It helps you to create your own data science projects.

**Course Objectives
**

This course is a benchmark for you to build a complex model in ease. It helps you to learn uncanny things and a good threshold to work with.

- Understand the fundamental of data science
- Understand the fundamental of data analytics
- Work with statistical analysis and business applications
- Write scripts in python
- Work with mathematical computing with Python (NumPy)
- Work with Scientific computing with Python (SciPy)
- Ability to work on data manipulation with pandas
- Understand Machine learning with Scikit
- Understand Natural Learning Processing with Scikit
- Work with data visualization in Python using matplotlib
- Understand web scraping with BeautifulSoup
- Able to integrate Python with Hadoop MapReduce and Spark

**Who should take up this Certification Course?
**

StepLeaf’s Python for DataScience Course is mainly preferred for Analytics Manager, Software Developers, Business Analytics, Integration specialists, Information Architects and Python professionals.

**What are the prerequisites for this course?
**

Little knowledge in the basics of Computer programming will explode your learning into a masterpiece.

Python, numpy, matplotlib, pandas, exceptionmanagement, functions, lambda, machinelearning, linearregression, gradientdescent, randomforest, confusionmatrix, decisiontree, dimensionalityreduction, naïvebayes, svm, supportvectormachine, associationrules, reinforcement, tsa, modelselection

**Learning Objectives:** You will get a brief idea of what Python is and touch on the basics.

**Topics:
**

- Overview of Python
- The Companies using Python
- Different Applications where Python is used
- Discuss Python Scripts on UNIX/Windows
- Values, Types, Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Command Line Arguments
- Writing to the screen

**Hands On/Demo:
**

- Creating “Hello World” code
- Variables
- Demonstrating Conditional Statements
- Demonstrating Loops

**Skills:
**

- Fundamentals of Python programming

- Python files I/O Functions
- Numbers
- Strings and related operations
- Tuples and related operations
- Lists and related operations
- Dictionaries and related operations
- Sets and related operations

- Tuple - properties, related operations, compared with a list
- List - properties, related operations
- Dictionary - properties, related operations
- Set - properties, related operations

**Skills:
**

- File Operations using Python
- Working with data types of Python

- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values
- Lambda Functions
- Object-Oriented Concepts
- Standard Libraries
- Modules Used in Python
- The Import Statements
- Module Search Path
- Package Installation Ways
- Errors and Exception Handling
- Handling Multiple Exceptions

- Functions - Syntax, Arguments, Keyword Arguments, Return Values
- Lambda - Features, Syntax, Options, Compared with the Functions
- Sorting - Sequences, Dictionaries, Limitations of Sorting
- Errors and Exceptions - Types of Issues, Remediation
- Packages and Module - Modules, Import Options, sys Path

- Error and Exception management in Python
- Working with functions in Python

- NumPy - arrays
- Operations on arrays
- Indexing slicing and iterating
- Reading and writing arrays on files
- Pandas - data structures & index operations
- Reading and Writing data from Excel/CSV formats into Pandas
- matplotlib library
- Grids, axes, plots
- Markers, colours, fonts and styling
- Types of plots - bar graphs, pie charts, histograms
- Contour plots

- NumPy library- Creating NumPy array, operations performed on NumPy array
- Pandas library- Creating series and dataframes, Importing and exporting data
- Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot

Skills:

Probability Distributions in Python

Python for Data Visualization

- Basic Functionalities of a data object
- Merging of Data objects
- Concatenation of data objects
- Types of Joins on data objects
- Exploring a Dataset
- Analysing a dataset

- Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
- GroupBy operations
- Aggregation
- Concatenation
- Merging
- Joining

- Python in Data Manipulation

- Python Revision (numpy, Pandas, scikit learn, matplotlib)
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Linear regression
- Gradient descent

- Linear Regression – Boston Dataset

- Machine Learning concepts
- Machine Learning types
- Linear Regression Implementation

- What are Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?

- Implementation of Logistic regression
- Decision tree
- Random forest

- Supervised Learning concepts
- Implementing different types of Supervised Learning algorithms
- Evaluating model output

- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA

- PCA
- Scaling
- Skills:
- Implementing Dimensionality Reduction Technique

- What is Naïve Bayes?
- How Naïve Bayes works?
- Implementing Naïve Bayes Classifier
- What is Support Vector Machine?
- Illustrate how Support Vector Machine works?
- Hyperparameter Optimization
- Grid Search vs Random Search
- Implementation of Support Vector Machine for Classification

- Implementation of Naïve Bayes, SVM
- Skills:
- Supervised Learning concepts
- Implementing different types of Supervised Learning algorithms
- Evaluating model output

- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How does K-means algorithm work?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How Hierarchical Clustering works?

- Implementing K-means Clustering
- Implementing Hierarchical Clustering

- Unsupervised Learning
- Implementation of Clustering – various types

- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How does Recommendation Engines work?
- Collaborative Filtering
- Content-Based Filtering
- Hands-On/Demo:
- Apriori Algorithm
- Market Basket Analysis

- Data Mining using python
- Recommender Systems using python

- What is Reinforcement Learning
- Why Reinforcement Learning
- Elements of Reinforcement Learning
- Exploration vs Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q – Learning
- α values

- Calculating Reward
- Discounted Reward
- Calculating Optimal quantities
- Implementing Q Learning
- Setting up an Optimal Action

- Implement Reinforcement Learning using python
- Developing Q Learning model in python

- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF

- Checking Stationarity
- Converting a non-stationary data to stationary
- Implementing Dickey-Fuller Test
- Plot ACF and PACF
- Generating the ARIMA plot
- TSA Forecasting

- TSA in Python

- What is Model Selection?
- The need for Model Selection
- Cross-Validation
- What is Boosting?
- How Boosting Algorithms work?
- Types of Boosting Algorithms
- Adaptive Boosting

- Cross-Validation
- AdaBoost

- Model Selection
- Boosting algorithm using python

Structure your learning and get a certificate to prove it.

**What are the system requirements for our Python Certification Training for DataScience?
**

The practical training is done in a Cloud Lab environment. This environment already has the required software in it.

**
How will I execute my practicals? **

The Case Studies are executed using Jupyter Notebook in Cloud Lab. The necessary instruction will be given by our StepLeaf instructor to execute all the assignments.

**
What are the Case studies for this course? **

There are totally 40 case studies as a part of this training. Given below are few of them.

**
Case Study 1 **

An Online Hotel booking application wants to create a recommendation of optimal suggestions for the users to book a hotel. Predict the hotel cluster for the user to book a room in a hotel using multi-class classification problems, build SVM and decision tree.

**
Case Study 2 **

In an Online Public Library users are requested to search for their individual choice of book. Using the ML model suggests that users read some more books based on his past purchase and refer to similar books read by other users. Help the library to find the error in their approach and build a profitable application.

**
Case Study 3 **

Do an end-to-end case study using time series analysis and forecasting with ML using Python. Extract meaningful statistics and find the insight of the data to predict future value with observed values.

**
****Case Study 4**

A construction company had a problem with its clients based on the quality of the building being constructed. Do an analysis and figure out all the different department in constructing a building and discover the problem and efficiency in each department which hinders the quality. Implement a proper solution to the problem

**
What are the projects for this course? **

There are totally 5 projects as a part of this training. Given below is one of them.

**
Description **

1. Download and install Python SciPy

2. Load dataset

3. Create 6 Machine Learning models, find the best with accuracy

StepLeaf has a study repository where you can find the recorded video of each class and all other essential resources for the course.