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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Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
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).
Today, the virtual world has transformed into real in too many ways to count. From cloud kitchens to real estate every business has an online presence; generating millions of data every single day. At the same time companies need data to estimate and decide on the future of a company. The work of a data scientist is to understand and codify data that will enable an organization to make comprehensive choices for their company. In such a situation, the demand for data scientists with excellent grasp of the medium becomes a necessary factor.
Delhi is not just any city, it is the national capital of India and is home to some of the most prestigious universities and leading companies in the field of data science such as Amazon, Crescendo, ZS, Michael Page, Engineer.ai, Emaar, Hike, Cube26 etc.
There are other factors that play an important role for data science becoming a popular career choice. They are:
This leads to increased need for data scientists in every sector and makes data science a coveted career choice for employees.
Technical skills are essential for working in data science. The good news is Delhi offers you a lot of scope in this space because it is home to some of the elite universities such as Indian School of Business and Finance, Indian Institute of Technology Delhi, Jawaharlal Nehru University, University of Delhi, Department of Computer Science, Indraprastha Institute of Information Technology, etc. Since, the work of a data scientist is to classify, process and analyze data they would need basic technical skills to adequately help a company make the best of the raw data available to them.
Following are the main technical skills that are a must for anyone considering a job as a data scientist:
Unstructured data: Data Scientists have to work with unstructured data which are not labeled or classified into database values. These include videos, social media posts, audio samples, customer reviews, blog posts etc.
Technical knowledge is not the only factor that determines the credibility of a Data Scientist. There are other factors that play a major role in how successful one will be in securing a Data Scientist job.
Asking ‘why’: Being continually curious is an important quality to have in a data scientist as he/she will work with a large amount of data.
Clarity: Having a clear idea of why you are working with a particular data set and what can be achieved from working on it will determine your quality as a data scientist.
Creativity: Data science is all about having a drive to make your work environment efficient. Thus having the creativity to constantly reinvent methods of processing and analyzing data will be an added advantage.
Questioning judgments: There is always a possibility of going overboard with one's creativity and questioning what can and cannot work is the prerogative of the data scientist.
Amazon, Crescendo, ZS, Michael Page, Engineer.ai, Emaar, Hike, Cube26 are some of the prominent companies operating in New Delhi. These companies make it beneficial for an aspiring data science professional to reside in the city. When more than half of the world’s population is using something you are an expert in, there will be certain benefits to it.
While you may become an expert in Data science, it is always preferred that you are up to date with the new developments in data science. For that you need to attend:
Data Science can be really grasped through constant practice and keeping yourself updated with every new programming and preprocessing or analytic skills. Even after securing a job one should continue working on individual projects and enter competitions to brush up as well as have fun with the skills of data science that might ignite your creative capacity.
Anything that gives insight to customer preferences is data. From your hospital prescription, stock investments, browsing history, or favorite color everything is data and can be used by companies to make ideal products, improving customer experience. Some of the major companies hiring skilled data scientists in Delhi are Amazon, Crescendo, ZS, Michael Page, Engineer.ai, Emaar, Hike, Cube26 etc.
The best way to master any technique is through practice and to master data science, the best approach would be to work through problems while solving data science algorithms. There are few data science problems which can be worked on to increase your skills in data science. The data science problems have different difficulty levels, making it easier for aspiring data scientists to choose the dataset problems they would prefer to work with according to their experience level.
The beginner level datasets can be easily solved by anyone who has basic ideas of mathematics and statistics. The intermediate level has regression problems which need some idea of coding which helps working with the large amount that consist the datasets. The advanced level requires an increased experience of the different aspects of data science to work quickly on the datasets which require intuitive and technical skills. Apart from getting experience, working on datasets is really interesting and fun to do. This makes it a positive experience without being bored. One can get different difficulty level datasets to work on from Analytics Vidhya website.
Due to the fact that Delhi is home to some of the best institutions in the country as well has some of the leading companies situated here, it makes living in Delhi highly beneficial for an aspiring data scientist.
The following points will guide you to become a successful data scientist,
Some of the most successful companies in the world rely on data science for their business growth. Below, listed, are the skill sets and steps you should take to become a data scientist-
IMS, IIT, Indraprastha Institute of Information Technology, etc are some of the globally recognized institutions which offer data science courses. The good news is that all of them are located in Delhi. This is highly beneficial for students who are preparing for a data science job.
Delhi is home to Indian School of Business and Finance, Indian Institute of Technology Delhi, Jawaharlal Nehru University, University of Delhi, etc. and these institutes are some of the best universities which offer advanced courses in the field of data science. The need for a master’s degree in Data Science depends on the degree one has pursued before. The necessity of a Master’s degree depends on the following points mentioned below. Score yourself according to the factors mentioned, if you score more than 6 points it is advisable that you get done with a master’s degree.
Programming is at the heart of data science and is an absolute must for anyone to learn in order to become a Data Scientist. It is not just the most essential aspect in Delhi but all over the world. If you want to become a data scientist, this is the first step which you must cross. The other skills are as follows:
Data sets: A job of a data scientist revolves around the analysis of a large number of data sets. Knowledge of programming is required to help you analyze those data sets.
Statistics: The ability to program goes hand in hand with your ability to use statistics. As you start working on programming, a lot of statistical techniques will be needed to make it easier for you to write code and create new statistical methods.
Framework: Having programming ability improves your efficiency and ability to structure the data. It is important that data scientists create frameworks for analyzing data so that visualization, interpretation and data pipeline are created, which will allow selected individuals to access the data at any time. Working with millions of data requires having a foolproof structure for storage of data and preventing it from being breached.
Making the work space efficient and secure is the ultimate responsibility of a data scientist.
In Delhi, a Data Scientist can earn up to Rs. 9,92,129 per year.
Delhi offers an annual salary of Rs. 9,92,129 per year as compared to Rs. 7,50,000 offered in Kolkata.
As opposed to the Data scientist’s average annual salary of Rs. 9,92,129 in Delhi, Data Scientists in Mumbai earn about Rs. 6,72,492 annually.
The average annual earnings of a Data Scientist in Delhi is Rs. 9,92,129 as compared to Rs. 6,15,496 earned by a Data Scientist in Bangalore.
The demand for Data Scientist far outweighs the supply. With all major, mid-sized and small-sized firms trying their hands on data science, the demand for Data Scientists in Delhi has only increased.
The benefit of being a Data Scientist in Delhi is that you can get an opportunity to work with all the major tech companies like Accenture, Deloitte, etc.
Delhi, the capital of India, is a hub for the tech companies. It is cheaper than other major tech cities like Bangalore. There are tons of conferences, meetups, and summits organized in the city for data scientists to attend. Also, Delhi is connected to Noida and Gurgaon that increases your job opportunities. As you have a key role in deciphering useful insights from raw data, it helps Data Scientists to get in touch with top-level executives. Also, being trained in data science gives you the freedom to work in any field that you want.
The major companies hiring Data Scientists in Delhi are IPSOS, Vehere, Global Analytics, Capillary, Accenture, IBM Research, Opera Solutions, etc.
|1.||PyData Delhi Meetup #31, Delhi, India||Saturday, May 11, 2019||UiPath Academy, Golf Course Road · Gurugram|
|2.||International Conference On Signal Processing And Big Data Analysis (ICSPBA-19), Delhi, 2019||15th May, 2019||Barakhamba Avenue, Connaught Place, Near Modern School, New Delhi, Delhi 110001|
1. PyData Delhi Meetup #31, Delhi
2. International Conference On Signal Processing And Big Data Analysis (ICSPBA-19), Delhi
|1.||PyData Delhi 2017|
2-3 September, 2017
|Indraprastha Institute of Information Technology Delhi, Shyam Nagar, Okhla Industrial Area|
|2.||International Data Science Summit, 2018||19th February, 2018||India Habitat Centre, Lodhi Road, Near Airforce Bal Bharati School, Institutional Area, Lodi Colony|
|3.||Data Science All Heads||July 21, 2018||CoWrks Tower A, Paras Twin Towers, Golf Course Road, Sector 54, Gurugram|
|4.||Developer Connect, Delhi||September 25, 2018||Hyatt Regency, Bhikaji Cama Place, Ring Road, New Delhi-110066|
1. PyData Delhi 2017, Delhi
2. International Data Science Summit, 2018, Delhi
3. Data Science All Heads, Delhi
4. Developer Connect, Delhi
The ideal path to securing a job as a data scientist is as follows:
To prepare to take an interview as a Data Scientist, the following ways might help you prepare well.
Study: Reread whatever you have learnt till now. There are few things you could brush up on:
Making data easy to infer from is the job of a data scientist. Below are some other Roles and Responsibilities of a Data Scientist:
The average salary for a Data Scientist is ₹ 10,02,509 per year in Delhi, which is 13% above the national average.
A data scientist not only analyzes data but finds the relevant ones and directs the future of a company by predicting future outcomes. Thus there are various roles and responsibilities of a data scientist that are a part of a data scientist’s career graph:
There are various ways one can look for possible employees:
Being the most popular career choice of 2019 there are various career opportunities for a Data Scientist-
Amazon, Crescendo, ZS, Michael Page, Engineer.ai, Emaar, Hike, Cube26, etc are either directly based or have a branch in New Delhi and are constantly in search of skilled data scientists. Below are the key points on which every data scientist is evaluated for being considered as a potential employee.
Data Science is a vast field which requires working with a large number of libraries. Finding the right programming language to master is, therefore, important for efficient working with all the libraries-
R programming: The only challenge of R is its steep learning curve, but it is an important language for various reasons
Python: With lesser packages than R, Python is still considered to be popular with data scientists. The reasons for that is-
SQL: Working on relational databases, Structured Query Language has-
Java: One of the oldest programming languages, Java has limited libraries limiting its potential. Nevertheless it has some advantages.
Scala: Working on JVM, it is considered rather complicated. But it does have some advantages:
The following are the steps to downloading Python 3 for Windows:
Download and setup: Go to the download page and setup your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use python’s functionalities from terminal.
Alternatively, you can also install python via Anaconda as well. Check if python is installed by running the following command, you will be shown the version installed:
python -m pip install -U pip
Note: You can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.
You can simply install python 3 from their 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, just follow the below steps:
/usr/bin/ruby -e "$(curl -fsS https://raw.githubusercontent.com/Homebrew/install/master/install)" Confirm if it is installed by typing: brew doctor
brew install python
You should 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