Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.
Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.
This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.Extensive hands-on labs and an interview prep will help you land lucrative jobs.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
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Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
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.
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.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
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.
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.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
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.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
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).
Crowned as the sexiest job of the 21st Century by the Harvard Business Review, Data Science is a field of opportunities. Gurgaon is home to many leading companies, including ZS Associates, Dell, Ericsson, Google, Ibibo India, Ixigo, Expedia, Microsoft, Oracle, Qualcomm, Royal Bank of Scotland, etc. These companies are constantly looking to add data science experts to make sense of their data. Some other reasons why data science is popular are:
The skills you need to become a data scientist include the following:
Below are the behavioural traits employers look for in a Data Scientist -
There are many benefits to being in the job declared as the ‘Sexiest job of the 21st century’ by Harvard Business review:
Below is the list of business skills needed to become a data scientist:
Here are some of the ways to brush up your data science skills:
Today’s world runs on data. Every company – big or small, MNC or a startup – has tremendous use of data produced every day. Associates, Dell, Ericsson, Google, Ibibo India, Ixigo, Expedia, Microsoft, Oracle, Qualcomm, Royal Bank of Scotland, etc. are some of the companies in Gurgaon looking for data scientist. Not just big MNCs but you can also find opportunities in small and mid size companies. Small companies use tools like Google Analytics as they have fewer resources as well as fewer data to work with. Mid-size companies have data but would need someone to apply ML techniques on it to leverage it.
To practise your data science skills, you should work on the following data science problems, categorized according to their difficulty level as compared to your expertise level:
Below are the right steps to becoming a successful data scientist:
Here is a list of key skills & steps required to get started:
Around 88% of data scientists hold a Master’s degree and 46% are PhD degree holders. A degree is very important because of the following:
If your total score adds up to more than 6 points, it would be advisable for you to earn a Master’s degree.
Yes, you need knowledge of programming to deal with the following elements:
Here is the logical sequence of steps you should follow to get a job as a Data Scientist.
Follow these steps to increase your chances of success at landing your dream job:
A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs.
Data Scientist Roles & Responsibilities:
Gurgaon is the hub of industrialization in India. Big and small companies grace its landscape. Due to high demand and less number of data scientists available, they earn base salaries up to 36% higher than other predictive analytics professionals. The salary of a data scientist depends on 2 things:
A career path in the field of Data Science can be explained in the following ways:
Business Intelligence Analyst: A Business Intelligence Analyst is an individual who has the job of figuring out the business as well as the market trends.
Data Mining Engineer: A Data Mining Engineer is an individual who has the job of not only examining the data for the needs of the business, but also for a third party.
Data Architect: The role of Data Architect is to work in tandem with system designers, developers and users in order to create blueprints that are used by data management systems.
Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses as well as the development of an understanding of data, so as to explore patterns from the given data.
Senior Data Scientist: A Senior Data Scientist is tasked with the anticipation of Business needs in the future and shaping the projects, systems and data analyses of today to suit those business needs in the future.
Below are the professional organizations data scientists can be a part of, regardless of their location:
There are several career options for a data scientist in Gurgaon 2019 –
Here is what employers look forward in Data Scientists:
Python is a multi-paradigm programming language and the inherent simplicity and readability of Python as a programming language makes it a language that is preferred by data scientists. Another great thing about Python which makes it the language of choice for data scientists is the broad and diverse range of resources that are available at the disposal of a data scientist, should he/she get stuck at a particular point. The Python community is all over the world. It is easy for a developer to get help in resolving his/her problems because the chances are that someone else had been stuck at the same problem in the past and its resolution has already been found.
As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.
Following are the steps to successfully install Python 3 on windows:
You can also install python via Anaconda if you wish. Check if python is installed by running the following command, you will be shown the version installed:
python -m pip install -U pip
Note that you can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.
You can install python 3 from its official website through a .dmg package, but we recommend using Homebrew to install python as well as its dependencies. To install python 3 on Mac OS X, follow:
$ xcode-select --install
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Check that it is installed by typing: brew doctor
brew install python
To confirm its version, use: python --version
We recommend that you also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.
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Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.
Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.
By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.
Tools and Technologies used for this course are
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Python and data science experts who have years of industry experience.
Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor