Certification in Data Science with Python

Objectives:

• At the end of this Module, you should be able to:
• Define Python
• Understand why Python is Popular
• Setup Python Environment
• Understand Operands and Expressions
• Write your First Python Program
• Understand Command Line Parameters and Flow Control
• Take Input from the User and Perform Operations on it
• Explain Numbers
• Explain Strings, Tuples, Lists, Dictionaries, and Sets

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
• 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

Objectives:

• At the end of this Module, you should be able to:
• Create and Execute Functions
• Learn Object Oriented Concepts
• Understand Python Standard Libraries
• Define Modules
• Handle the Exceptions

Topics:

• 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

Objectives:

• At the end of this Module, you should be able to:
• Create Arrays using NumPy
• Perform Various Operations on Arrays and Manipulate them
• Read &Write Data from Text/CSV Files into Arrays and vice-versa
• Create Series and Data Frames in Pandas
• Indexing and Slicing of Data Structures in Pandas
• Reading and Writing Data from Excel/CSV Formats into Pandas
• Data Preparation
• Merging, Concatenation, Combining, Pivoting &Removal
• Data Transformation – Merging, Joining & Concatenation
• Create Simple Plots using Matplotlib
• Learn Different Plot Formats Available in Matplotlib
• Choose the Right Plot Format for a Problem at Hand Judiciously
• Scale and Add Style to your Plots

Topics:

• NumPy – Arrays
• Operations on Arrays
• Indexing, Slicing and Iterating
• Reading and Writing Arrays on Files
• Pandas – Data Structures & Index Operations
• 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
• Reading and Writing Data from Excel/CSV Formats with Pandas
• The Matplotlib Library
• Grids, Axes, Plots
• Markers, Colours, Fonts and Styling
• Types of Plots – Bar Graphs, Pie Charts, Histograms
• Contour Plots

Objective:

At the end of this module, you should be able to:

• Essential Python Review
• Necessary Machine Learning Python libraries
• Define Machine Learning
• Discuss Machine Learning Use cases
• List the categories of Machine Learning
• Illustrate Supervised Learning Algorithms
• Identify and recognize machine learning algorithms around us
• Understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function and optimization.

Topics:

• 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

Objective:

At the end of this module, you should be able to:

• Understand What is Supervised Learning?
• Illustrate Logistic Regression
• Define Classification
• Explain different Types of Classifiers such as,
• Decision Tree
• Random Forest

Topics:

• What is 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?

Objective:

At the end of this module, you should be able to:

• Define the importance of Dimensions
• Explore PCA and its implementation
• Discuss LDA and its implementation

Topics:

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

Objective:

At the end of this module, you should be able to:

• Understand What is Naïve Bayes Classifier
• How Naïve Bayes Classifier works?
• Understand Support Vector Machine
• Illustrate How Support Vector Machine works?
• Understand Hyper parameter Optimization

Topics:

• 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

Objective:

At the end of this module, you should be able to:

• Define Unsupervised Learning
• Discuss the following Cluster Analysis
• K – means Clustering
• C – means Clustering
• Hierarchical Clustering

Topics:

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

Objective:

At the end of this module, you should be able to:

• Define Association Rules
• Learn the backend of recommendation engines and develop your own using python

Topics:

• What are Association Rules?
• Association Rule Parameters
• Calculating Association Rule Parameters
• Recommendation Engines
• How Recommendation Engines work?
• Collaborative Filtering
• Content Based Filtering

Objective:

At the end of this module, you should be able to

• Explain the concept of Reinforcement Learning
• Generalize a problem using Reinforcement Learning
• Explain Markov’s Decision Process
• Demonstrate Q Learning

Topics:

• 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
• values

Objective:

At the end of this module, you should be able to:

• Explain Time Series Analysis (TSA)
• Discuss the need of TSA
• Describe ARIMA modelling
• Forecast the time series model

Topics:

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

Objective:

At the end of this module, you should be able to:

• Discuss Model Selection
• Define Boosting
• Express the need of Boosting
• Explain the working of Boosting algorithm

Topics:

• What is Model Selection?
• Need of Model Selection
• Cross – Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms
• Adaptive Boosting

You don’t have to worry about the System Requirements as we are having state of the art Training Labs with Cloud access. These Labs already contains all the necessary software that will be required to execute your practical’s. You can access Cloud Environment from anywhere on your browser.

You will do your Assignments/Case Studies on your Cloud LAB environment that you can access from our state-of-the-art Training Lab as well as from home using browser. For any doubt, the 24*7 support team will promptly assist you.

Project #1:

Industry: Social Media
Problem Statement: You as ML expert have to do analysis and modeling to predict the number of retweets of a tweet given the input parameters.

Actions to be performed:

Load the corresponding dataset. Perform data wrangling, visualization of the data and detect the outliers, if any. Use the plotly library in Python to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your goal. Also, use scaling processes, PCA along with boosting techniques to optimize your model to the fullest.

Project #2:

Industry: HealthCare
Problem Statement: You as an ML expert have to cluster the county/state based on various healthcare plan data provided to you across years.

Actions to be performed:

You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that, you have to bring the exports (in tons) of all counties/states down to the same scale across years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principal components which explain the max variance.

The projects that are picked up to be part of assessment are the most updated and real-world projects. The learning you acquire from the training can be applied to complete those projects. These projects test your learning and concepts you acquired during the training program. The completion of these projects makes you corporate ready where you can exhibit your learning and skills.

DevLabs Alliance has experts and practitioners who are having comprehensive experience on Data Science, Machine Learning, Python and related technologies. We ensure that the participants work on the assignments and practical along with trainer. It’s not only an instructor-led training but the workshop where the participants get the full exposure to theory and practical.

We arrange some extra time so that you can align yourself with the ongoing class.

For Virtual Instructor-Led (IVLT) training:

You are requested to raise the refund request within 7 days of the beginning of the first batch in which you are eligible to attend. Refund/Money back does not apply in any other condition, except the batch is cancelled by DevLabs Alliance due to instructor unavailability insufficient enrollments, or forced measures(like floods, earthquakes, national/global pandemic, political instability, etc.). However, the learner can choose to join another batch of the same training/course after discussing with DevLabs Alliance management.

For refund conditions, please refer to the “Refund” section below.

For Classroom Training

We may need to postpone/cancel an event because of many reasons, such as insufficient enrollments, instructor unavailability, or force majeure events (like floods, earthquakes, political instability, etc). For refund conditions, please refer to the “Refund” section below.

For Refund(applicable for IVLT and Classroom training)

If we cancel an event, 100 percent of training/course fees will be refunded to the learner.

If a cancellation is done by an attendee, 7 days (or more) before the batch begins, a total of 10% of the total paid fee will be deducted and the remaining amount will be refunded to the learner.

If a cancellation is done by a learner within 7 days (or less) of the start of the batch or after attending a maximum of 2 classes, 25% of the total paid fee will be deducted and the remaining amount will be refunded to the learner.

Refund requests can be initiated by sending emails to the DevLabs Alliance team at training@devlabsalliance.com or accounts@devlabsalliance.com. Refund request usually takes 3-4 working days to get approved by Finance and then 10-15 working days to get the payment back into the source account of the learner.

By the end of each session you will get study materials and Assignments will be provided as per planned syllabus, distribution of these recordings without DevLabs Alliance permission is not allowed.

You can choose any upcoming training and we can accommodate you in that.

Yes, once your assessment process is complete then certificate will be awarded to you.

No, your certificate will be valid for life time.

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