Certification in Data Science with Python

DevLabs Alliance brings you a comprehensive Data Science certification course in Python that enables you to understand Python programming language from basics through advance topics. This Course will help you comprehend important Python programming concepts such as Data & File Operations, Object-Oriented Concepts in Python & various Python libraries such as Matplotlib, Pandas and Numpy etc., and hence, enabling you to delve in Data Science. As a result of it, you gain proficiency in various EDA and Machine Learning Algorithms such as Regression, Clustering, Decision Trees, Random Forest, Naïve Bayes and Q-Learning.

Data Science Certification Training program exposes you to concepts of Statistics, Time Series and different classes of Machine Learning Algorithms like Supervised, Unsupervised and Reinforcement Learning Algorithms. Throughout the Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR. DevLabs Alliance’s Data Science Certification Training course opens horizon in Data Science career.

Key Features


  • 32 hours of practical oriented training cum workshop

  • Each session followed with exercises and Project Work

  • Certification Guidance

  • Industry specific project implementation

  • Flexible Schedule

  • Doubt Clearing and Technical Support

Trainings for

Individual Classroom LearningCorporate Training Solutions
Instructor led Face 2 Face Practical Oriented TrainingFace 2 Face Interactive Practical Oriented training
State of the Art Training LabsLearn as per full day schedule with discussions and exercises.
Flexible ScheduleDoubt Clear sessions
Technical SupportCompletely Customizable course content and schedule based on your need
Use cases implementationsCertification Guidance Provided
Certification Guidance ProvidedCase studies and Use cases implementations

Why this Course?


  • Python is the future of AI and Machine Learning – Adrian Rosebrock, Author of the book, ‘Deep Learning for Computer Vision with Python’

  • Businesses Will Need One Million Data Scientists by 2020 – Kdnuggets

  • Roles like Chief Data Scientist & Chief Analytics Officers have emerged to ensure that analytical insights drive business strategies – Forbes

  • Data Scientist has been named the best job in America for 2018 with median base salary of $242,000 and 4,524 job openings – Forbes

About the Data Science Certification – Python Course

DevLabs Alliance’s Data Science Certification – Python Course will primarily cover the concepts of Python like object-oriented concepts, sequences, file operations and some of the extensively used Python libraries which include pandas, numpy, matplotlib, etc. and steadily converge towards Machine Learning and its detailed mechanism.

As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be trained on Reinforcement Learning which in turn is a vital characteristic of Artificial Intelligence. You will be able to learn your machine based on real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.

Why learn Data Science?

Data Science is a set of techniques that enable computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science.

This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensionality reduction, model evaluation and exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.

Now using python for the same really adds to your value as Python has been one of the premier, flexible, and powerful open-source languages that are easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. Also, it is the most preferred language for Artificial Intelligence, Robotics, Web Development and DevOps.

Creating Next Gen Engineers!

What are the objectives of our Data Science Certification Course – Python?

After completing this Data Science Certification Course – Python training, you will be able to:

  • In-depth understanding around Machine Learning concepts
  • Learn data visualization techniques
  • Learn techniques to handle various types of data – ordinal, categorical, encoding
  • Learn different tools and methods for predictive modelling
  • Understand Roles played by a Machine Learning Engineer
  • Perform Sentimental analysis
  • Programmatically download data and analysis on it
  • Work with real time numbers
  • Understand Time Series and its associated concepts

Who should go for this Data Science Certification Course – Python?

DevLabs Alliance’s Data Science certification course – Python is appropriate for the below professionals:

  • Programmers, Software Developers, Software Testing Professionals, Technical Leads, Architects
  • IT Leaders who want to set the direction of their organization in Data Science and Machine Learning
  • Developers aspiring to be a ‘Machine Learning Expert’
  • Graduates who are looking to make a career in Data Science
  • Any professional who wants to understand how predictive analysis system works

What are the pre-requisites for Data Science Certification Course – Python?

The pre-requisites for the Data Science Certification Course – Python include basic understanding of Computer Programming Languages.  Having experience working on any of the data analysis tools like SAS/R will be a plus and statistics. However, we will provide you complementary self-learning material on “Statistics for Data Science” once you enroll for the training program.

Certification

DevLabs Alliance’s Data Science Certificate Holders work at 1000s of companies like Accenture, Ciena, Optum, Oracle etc.

To unlock DevLabs Alliance’s certificate You have to complete and submit the use case assignments provided during the course, then your submitted assignments will be evaluated by a team of Big data experts and practitioners. Your result will be shared after assessment and certification will be awarded accordingly.

Why you should take this course from DevLabs Alliance

FeaturesDevLabs AllianceOther Training Providers
Classroom SessionInteractive Classes room session with Extensive hands-onX - Instructor led, no hands-on
1-1 Training

Training ScheduleFlexibleFixed
Customized Course

Access to Recorded Videos

EMI Options

Support Post Session

Case Studies Discussion

1
Module 1: Introduction to Python, its Sequences and File Operations

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
2
Module2: Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

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
3
Module3: Data Manipulation using NumPy, Pandas & Matplotlib

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
4
Module 4: Introduction to Machine Learning with Python

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
5
Module 5: Supervised Learning – I

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?


6
Module 6: Dimensionality Reduction

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


7
Module 7: Supervised Learning – II

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


8
Module 8: Unsupervised Learning

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?


9
Module 9: Association Rules Mining and Recommendation Systems

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


10
Module 10: Reinforcement Learning

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


11
Module 11: Time Series Analysis

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


12
Module 12: Model Selection and Boosting

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.
You will be eligible for ninety percent refund until one week before commencement of a course.
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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Enrolled: 250 students
Duration: 32 Hours
Modules: 12
Level: Advanced

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