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.
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).
Harvard Business Review called Data Scientist the sexiest job of the 21st Century in 2012. Data is everywhere around us and Data-driven decision making is the need of the hour. From making effective business decisions to classifying target audiences, data science offers great value to businesses. Noida bustles with some of the best companies to work for, including Paytm, Cadence Design Systems, Adobe, Ericsson, etc. All these companies are looking for expert data scientists to help them take informed decisions based on their findings.
Below are the top technical skills required to become a data scientist:
Below are the behavioural traits of a successful Data Scientist -
Here are the 5 proven benefits of being a Data Scientist in Noida:
Below is the list of business skills needed to become a data scientist:
Following are some of the ways to brush up your data science skills:
We live in a world of data. Noida was ranked as the Best City in Uttar Pradesh and is home to several leading companies like HCL Technologies, Monotype, Tata Consultancy Services, Tech Mahindra, Infogain, Paytm, etc. Whether it’s a startup or an MNC, Data is valuable to all these companies as it tells them about their audience’s interests, allowing them to improve their customers’ experiences. So, all these companies are looking for skilled data scientists to do the job.
We’ve compiled a list of data sets you can practice on, categorized on the basis of difficulty:
Below are the steps to becoming a successful data scientist in Noida:
We have compiled a list of needed key skills & steps required to get started in this direction:
A degree is helpful because of the following –
If your score is more than 6 points, you should get a Master’s degree:
Yes, programming knowledge is a must in the field of Data Science because of the following reasons:
If you want to get a job in the field of Data Science, you need to follow this path:
Follow the below steps to increase your chances of success:
The data generated every day is a gold mine of patterns and ideas that could prove to be very helpful for making key business decisions. It is the responsibility of a data scientist to extract the relevant information and make sense of it.
Data Scientist Roles & Responsibilities:
The average salary for a Data Scientist is ₹ 6,65,636 per year in Noida, Uttar Pradesh.
A career path in the field of Data Science in Noida 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 examining the data for the needs of the business. He also needs to create sophisticated algorithms that further aid in the analysis of data.
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.
Referrals are the most effective way to get hired. Some of the other ways to network with data scientists are:
There are several career options for a data scientist in Noida today –
Companies generally prefer data scientists to have mastery over some software and tools. They generally look for:
The simplicity of Python makes it popular among data scientists. It is a structured and object-oriented programming language that contains several libraries and packages that are useful for the purposes of Data Science. The Python community is another big advantage. There are millions of developers working on the same problems with the same programming language every day. They form forums, communities and clubs to interact with each other and help solve problems.
Below are the most popular programming languages used in the Data Science field apart from Python:
Following are the steps to install Python 3 on windows:
python -m pip install -U pip
You can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.
To install python 3 on Mac OS X, follow the below steps:
$ xcode-select --install
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Confirm the same by typing: brew doctor
brew install python
To confirm its version, use: python --version
Note: It’s advisable to 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.
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Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor