Data Science with Python Rated 4.0/5 based on 24 customer reviews

Data Science with Python Training in Hyderabad, India

Master Python to start a career in Data Science

  • 42 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms
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Why should you learn Data with Python science?

Data Science has been among the top paying jobs for the past several years. The rise of big data and use of analytics to fuel business growth has made it among the most in-demand jobs in enterprises and data scientists, a revered class of professionals.

Start your path to becoming a Data Scientist, using the power of Python. Analyze data, create beautiful visualizations, and use powerful machine learning algorithms to convert your data into meaningful statistics that can help organizations achieve business outcomes.

How do you get started with Python for Data science?

This interactive and comprehensive course is a great place for you to get started on Python programming language and its use in Data Science.This Data Science with Python course from Zeolearn aims at helping you understand the core concepts of Data science including exploratory data science, statistics, hypothesis testing, regression classification modeling techniques, data visualization and machine learning algorithms. Coaching from experts and plenty of hands-on exercises will ensure that you are industry ready by the end of this workshop.

Zeolearn Experience

Learn By Doing

Immersive Hands-on training with combination of theoretical learning, hands-on exercises, group discussions, assignments and intensive Q&A sessions.

Live & Interactive

Ask questions, get clarifications, and engage in discussions with instructors and other participants.

Mentored by Industry Experts

Get Mentored by Industry practitioners having more than 10 years of experience.

Reason based learning

Don’t gain just theoretical or practical knowledge. Understand the WHAT, WHY, and HOW of a subject. Simplify the subject matter and get in-depth comprehension.

Code Review by Professionals

Get reviews and timely feedback on your assignments and projects from professional developers.

Build Projects

We emphasize on learning the concepts through examples and help you in building a portfolio of projects through the course of training.

Lifetime Enrolment

Free lifetime enrolment into any of the upcoming batches to help you refresh the concepts.

Curriculum designed by Experts

The Curriculum goes through multiple levels of design and preparation by the experts to keep the topics/modules relevant to everyday changes in technology.

Study even from remote locations

Learn to use collaborative mediums to share opinions and improve your coding skills with assistance from the instructors and other participants.

Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

What you will learn

1. Tools & Technologies

Get acquainted with various analysis and visualization tools such as Matplotliband Seaborn

2. Statistics for Data Science

Understand the behaviour of data; build significant models using concepts of Statistics Fundamentals

3. Python for Data Science

Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel

4. Exploratory Data Analysis

Use Python libraries and work on data manipulation, data preparation and data explorations

5. Data Visualization

Use of Python graphics libraries like Matplotlib, Seaborn etc.

Prerequisites

There are no prerequisites to attend this course, but elementary programming knowledge will comein handy.

Projects

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month.

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Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Curriculum

Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Projects
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. 

You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. 

You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study. 

Topics

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data
  • Case Study

Hands-on:

  • Know how to install Python distribution like anaconda and other libraries
  • Write Python code for defining your own functions. Learn the object oriented way of writing classes and objects
  • Write Python code to import dataset into Python notebook
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset

Learning Objectives:

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

Hands-on:

Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives:

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on:

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling

Learning Objectives:

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on:

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various kinds. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.

Work on a real- life Case Study with ARIMA.

Topics

  • Understand Time Series Data
  • Visualizing TIme Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:

  • Write Python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
  • Write Python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices. 

Learning Objectives:

A mentor guided real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

Project to be selected by candidates.

FAQs

The Course

  • Get advanced knowledge of data science and how to use it in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Master Python and its libraries

Data Scientists are among the highest paid technology professionals in the world. Businesses are increasingly relying on analytics and statistics to expand and improve their products as a result of which data analysis is being used in marketing, supply chain, human resource and other areas.


Python is a user friendly language and in much use as a tool in data analysis. Being an expert in Python and its libraries and using them for data analysis are skills that will not become redundant for a long time. Hence this is the right time to enrol in this course.

There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

  • Those Interested in the field of data science
  • Those who want to master Python
  • Those working with and analysing large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

Data Science with Python workshop experience

The workshops at Zeolearn are always interactive, immersive and intensive hands-on programs. There are 3 modes of Delivery and you can select based on the requirements -

  • Online Classroom training: Learn from anywhere through the most preferred virtual live instructor led training with the help of hands-on training and interactive sessions
  • One-to-One Training: You can enrol for one-to-one Data Science with Python classroom training session with our expert trainer at a preferred time. With this mode, you can customize your curriculum to suit your learning needs.
  • Team/Corporate Training: In this type of training, an Organization can nominate their entire team for online or classroom training. You can customize your curriculum to suit your learning needs and also get post-training expert’s support to implement Data Science with Python concepts in the project.

We follow the below mentioned procedure for all the training programs by dividing the complete workshop experience into 3 stages i.e Pre, Workshop and Post. This is a tried and tested approach using which we have been able to upskill thousands of engineers.


Pre-training

Before the start of training program, we make sure that you are ready to understand the concepts from Day 1. Hence, as a process of preparation for the intensive workshop, we provide the following -

  • Reference articles/ Videos and e-books
  • 2-4 hrs of training on pre-requisites - to make you workshop ready
  • Pre-Workshop Assessments - to assess and benchmark
  • Environment set-up docs


During Training

The training is completely hands-on and you receive the below mentioned deliverables from Zeolearn team -

  • PPT and Code Snippets used in the class
  • Learners Guide or E-book
  • Projects / Case Studies
  • Assessments / Lab exercises
  • Quizzes and Polls
  • Study Plans - To structure your learning.


Post Training

We don’t just impart skills but also make sure that you implement them in the project. And for that to happen, we are always in touch with you either through newsletters or webinars or next version trainings. Some of the post-training deliverables lined-up for you are -  

  • Project assistance with mentor
  • Course Recordings
  • Access to Alumni Network
  • Additional workshops on advanced level concepts
  • Regular emails/newsletters on Blogs/Tutorials and other informational content

Yes, Zeolearn has well-equipped labs with the latest version of hardware and software. We provide Cloudlabs to explore every feature of Data Science with Python through hands-on exercises. Cloudlabs provides an environment that lets you build real-world scenarios and practice from anywhere across the globe.  You will have live hands-on coding sessions and will be given practice assignments to work on after the class. 


At Zeolearn, we have Cloudlabs for all the major categories like Web development, Cloud Computing, and Data Science.

Covers Exploratory Data Analysis, Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, Recommender Engines, Text Mining, ANN, SVM, K means Clustering, Ensemble Machine Learning Techniques


PROJECT-1

TITLE - Predict House Price using Linear Regression
DESCRIPTION - With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.


PROJECT-2

TITLE - Predict credit card defaulter using Logistic Regression
DESCRIPTION - With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.


PROJECT-3

TITLE - Predict chronic kidney disease using KNN
DESCRIPTION - Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.


PROJECT-4

TITLE - Predict quality of Wine using Decision Tree

DESCRIPTION - Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

This course is delivered by industry-recognized experts who would be having more than 10 years of real-time experience in Data Science with Python.


Not only will they impart knowledge of the fundamentals and advanced concepts, they will provide end-to-end mentorship and hands-on training to help you work on real-world projects with regards to Data Science with Python.

Once you register for the course you will be provided with system requirements and lab setup document which contains detailed information to prepare the environment for the course.

To attend Data Science with Python training program, the basic hardware and software requirements are as mentioned below -


Hardware requirements

  • 4 GB RAM
  • 3-4 GB of free space

Software Requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular linuxflavors
  • Latest Chrome browser

Permissions Required

  • Internet Access

Data Science with Python Online Training Experience

All our training programs are quite interactive and fun to learn with plenty of time spent on lot of hands-on practical training, use case discussions and quizzes. Our instructors also use an extensive set of collaboration tools and techniques which improves your online training experience.

This will be live interactive training led by an instructor in a virtual classroom.

You will receive a registration link to your e-mail id from our training delivery team. You will have to log in from your PC or other devices.

Yes, for all the online public workshops there would be participants logging in from different locations.

In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: http://www.zeolearn.com/contact-us, or send an email to hello@zeolearn.com.


We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, we will facilitate a one-on-one discussion session with our trainers.

If you miss a class, you can access the class recordings anytime from our LMS. At the beginning of every session, there will be a 10-12 minute recapitulation of the previous class. You can watch the online recording and clarify your doubts at that time. You may need to login 15 minutes before the main lecture begins to avail this facility.


We also have a Free Lifetime enrollment for most of our courses. In case you miss out a class, you can also enroll for another complete workshop or only for a particular session.

Finance related

Typically, Zeolearn’s training are exhaustive and the mentors help you out in understanding the in-depth concepts.


However, if you find it difficult to cope, you may discontinue within the first 4 hours of training and avail a 100% refund. Learn more about our refund policy here.

Zeolearn offers a 100% money back guarantee if the candidates withdraw from the course right after the first session. To learn more about the 100% refund policy, visit our refund page.

Yes, we have scholarships available for Students and Veterans. We do provide grants that can vary upto 50% of the course fees.


To avail scholarships, please get in touch with us at hello@zeolearn.com. The team shall send across the forms and instructions to you. Based upon the responses and answers that we receive, the panel of experts take a decision on the Grant. The entire process could take around 7 to 15 days.

Yes, we do have instalment options available for the course fees. To avail instalments, please get in touch with us at hello@zeolearn.com. The team shall explain how the instalments work and would provide the timelines for your case.


Usually we allow payment in 2 to 3 instalments but have all to be paid before you complete the course.

Have More Questions?

Mail Us support@zeolearn.com

Data Science with Python Course in Hyderabad

Data Science with Python Foundation

The Python language is simple and flexible. It is undoubtedly the appropriate choice for statistics. Some of the highlights of using Python language are speed, productivity, extensive libraries etc. These factors have contributed to the field of data analysis to a great extent.

The Zeolearn Institute offers a comprehensive course for the beginners on data science with python foundation. The python for data science course in Hyderabad is best suited for programmers and those who aspire for a career in data science. Python language is up to date with the technological demands. Thus studying a course about this language is an additional qualification for programmers to mention in their resumes.

Key Features of the course

The data science with python training in Hyderabad comprises of 24 hours of immersive instructor-led training and interactive, hands-on learning sessions. After the registration, the students will be provided with free study materials for future references. The students will be trained to create a Python program to analyse data sets and models to solve complex problems. During the python for data science training in Hyderabad, the participants will learn about Python libraries for effective data processing and analysis.

Benefits of the data science with python classes in Hyderabad

During the training session, the students will be exposed to the fundamental concepts of Python including the data types, OOPs concepts etc. The students will develop an understanding about Python lists, functions and packages.

After the completion of the course, the students will receive data science with python certification in Hyderabad. The coaching is provided at an affordable fee for the students. The participants of data science with python online in Hyderabad must know basic arithmetic, algebra vectors etc. Thus joining the Zeolearn Institute here will be the right decision to ace Python for data science.

 
 
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