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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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).
Data is everywhere around us. There are more electronic communication devices on this earth than ever. Each of these devices produce millions of data every single day. It becomes essential in such a situation to find a way to harness that data to take forward business opportunities and make predictions for the future of an organization. Data Science is the collection, classification and analysis of data for the purpose of understanding the consumer needs and requirements, to find the underlying patterns in the creation of data and optimizing business strategies.
Thanks to the rapid generation of data and the need for making sense of it all, data scientists are in huge demand right now. Their particular skill set makes them the prized unicorn that can help an organization make important marketing decisions. In New York, companies like Amazon Web Services, Google, Morgan Stanley, Macy’s, Defined Clarity, Liquidnet, Spotify, Bowery Valuation, etc. are looking for data scientists to help them make sense of their data. Due to the versatility of data use, there evolves various ways in which becoming a Data Science professional can have its advantages:
Data Science is a lucrative opportunity not only for the industrial or commercial sector to increase their business but also for the employees in those sectors.
New York is the home of several institutions that offer Master’s degree in Data Science including Syracuse University, Clarkson University, Columbia University, Cornell University, Cuny Bernard M Baruch College, Fordham University, Icahn School of Medicine, Keller Graduate School of Management, Manhattan College, Marist College, New York Institute of Technology, New York University, Pace University, Pratt Institute – Main, Rochester Institute of Technology, St. John’s University, University of Buffalo, University of Rochester, etc. These programs will help you acquire all the technical skills required to become a Data Scientist. The essential skills needed to become a Data Scientist are as follows:
Below are 5 behavioral traits needed to become a successful data scientist:
Today, almost every industry collects data from customers. This has caused an increase in demand for data scientists who can use their skills to make sense of this data. In New York, there are several organizations that are looking for data scientists to join their team including Hearst Magazines, A+E Networks, Honcker Inc., Dow Jones, Citizen, AdTheorent, Disney Streaming Services, Viacom, Legends, Milliman, Conde Nast, Reorg Research, WeWork, The CARIAN Group, Dow Jones, Legends Hospitality, Element Global Search, T. Rowe Price, YouTube, CBS, London Stock Exchange Group, AIG, Otis Wealth, ViaVan, Ocrolus, etc. When more than half of the world’s population is using something you are an expert in there will be 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. Below are some ways to brush up your skills as a data scientist:
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 your skills as well as have fun with data science to ignite your creativity.
According to Harvard Review 2012, Data Scientist is the sexiest job of the 21st century. Owing to the large demand and low supply issue, data scientists are paid handsomely. New York is home to several companies that are looking for data scientists to join their team and help them optimize their business processes and marketing strategies. These companies include Amazon Web Services, Google, Morgan Stanley, Macy’s, Defined Clarity, Liquidnet, Spotify, Bowery Valuation, Hearst Magazines, A+E Networks, Honcker Inc., Dow Jones, Citizen, AdTheorent, Disney Streaming Services, Viacom, Legends, Milliman, Conde Nast, Reorg Research, WeWork, The CARIAN Group, Dow Jones, Legends Hospitality, Element Global Search, T. Rowe Price, YouTube, CBS, London Stock Exchange Group, AIG, Otis Wealth, ViaVan, Ocrolus, 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 a few data science problems which can be worked on to improve your skills in data science. They are categorized below according to their difficulty level:
ImageNet Data Set: This data set is a unique one for it includes lots of different variables like object detection, localization, classification and screen parsing. There are a number of images that are easily available and you can create your project around any of them. As recorded till now, the search engine has over 15 million images together creating around 140gb of data.
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. Google, Amazon ,Facebook or Twitter have the highest rate of employing data scientists. In such a scenario what should you do to get ahead of your peers? Below, are the steps you should follow:
If you want to earn a degree in Data Science in New York, you can try the data science programs in colleges like Syracuse University, Clarkson University, Columbia University, Cornell University, Cuny Bernard M Baruch College, Fordham University, Icahn School of Medicine, Keller Graduate School of Management, Manhattan College, Marist College, New York Institute of Technology, New York University, Pace University, Pratt Institute – Main, Rochester Institute of Technology, St. John’s University, University of Buffalo, University of Rochester, etc. As mentioned above, most data scientists are Master’s or PhD degree holders. Around 75% are PhD scholars with some background in computer science, mathematics or social sciences. Some of the benefits of getting a degree in Data Science important to get a Data Science job-
Networking: Interacting with your peer group will increase your conceptual clarity and you will find networking opportunities. Having acquaintances in the industry always gives people an edge.
Structured learning: Having a schedule for your curriculum will not only provide a holistic idea about the discipline, but also ensure thorough learning.
Internships: Getting a hands-on experience by doing internships can be very helpful and provide you with an idea about the workload you will be expected to do.
Appropriate academic degrees and qualification: While having a degree from a prestigious university does provide an advantage to your career it is also important that you have a relevant degree.
Education over experience: Depending on where you want to work you should consider getting a Master’s or PhD degree. If you are considering a job in the Fortune 500 then it is better to get a decent degree from a reputed university. A master’s degree as a criterion for employment depends on the quality of the program the candidate followed. If you have practical skills to offer through professional experiences, a master’s degree will not be necessary.
Thus it is important to have a clear goal at the earliest about which sector one can or wants to work in, so that he/she can pursue the right degree or get the appropriate experience.
There are several universities in New York that offer postgraduate programs in Data Science. But before you apply for a Master’s degree, you need to know if you really need one or not. 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 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. The other reasons are as follows:
Data sets: A job of a data scientist revolves around 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 be identified which in turn will make it easier for you to code and create new statistical methods. Without the knowledge of implementation of statistics in data science, statistics will prove to be useless.
Framework: Having programming ability improves an individual's 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 is constructed 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 prevent it from being breached.
Making the work space efficient and secure is the ultimate responsibility of a data scientist.
A Data Scientist based in New York earns $99,716 per year on an average.
As compared to Los Angeles, Data Scientist in New York earn $1,442 more per year, with an average annual salary of $98,294 per year.
The average annual salary of a data scientist in New York is $99,716, which is $6,750 less than that of Seattle.
The data scientists earn an average of $99,716 in New York as compared to $110,925 in Chicago.
The city of Buffalo in New York state offers a data scientist an average pay of $93,690 which is slightly lower than the salary earned by data scientists in New York.
Apart from New York city, the city of Rochester in New York state has an average pay of $78,611 per year for data scientists.
In the New York State, the demand for Data Scientists is quite high. New York is home to several major organizations that have now started using Data Science to use their raw materials into useful insights. This has also increased the need of Data Scientists.
The benefits of being a Data Scientist in New York are:
Being a data scientist comes with a lot of perks and advantages. Apart from the salary, these perks include the ability to gain attention of top-level executives as they are responsible for delivering useful insights by analyzing raw data. Also, Data Scientists have the luxury to work in their chosen field of interest. So many companies from different fields have started to hire Data Scientists. This, in turn, has given them the opportunity to select the field they are interested to work in.
Amazon, Digital Ocean and Aetna are among the companies that are recruiting data scientists in New York.
|1.||Data Science Salon | NYC 2019||June 13, 2019|
VIACOM 1515 Broadway 2nd Floor New York, NY 10036 United States
|2.||The Business of Data Science - New York||11 June, 2019 to 12 June, 2019|
Downtown Conference Center 157 William Street New York, NY 10038 United States
|3.||Building your Data Science Toolbox||June 27, 2019|
General Assembly 902 Broadway New York, 10010 United States
|4.||NYC Summer Accelerator in Data Science & Analytics 2019||15 July, 2019 to 14 Aug, 2019|
Midtown New York, NY 10036 United States
|5.||Ethical Data Collection for Nonprofits||May 2, 2019|
Civic Hall 118 W 22nd St 12th Floor New York, NY 10011 United States
|6.||Big Data Finance 2019||9 May, 2019 to 10 May, 2019|
Cornell Tech 2 West Loop Road New York, NY 10044 United States
|7.||Data Analysis and Linearization in Physics||24 July, 2019 to 26 July, 2019|
Teachers College, Columbia University 525 W 120th St Zankel Hall New York, NY 10027 United States
|8.||Intro To Python For Microsoft Excel & Data Analysis||May 8, 2019|
Byte Academy 295 Madison Ave Fl 35 New York, NY 10017 United States
|9.||Machine Learning Immersive||10 June, 2014 to 14 June, 2014|
Practical Programming 115 West 30th Street 5th Floor New York, NY 10001 United States
|10.||Dataware Hands-On Labs New York||October 10, 2019|
JW Marriott Essex 160 Central Park S New York, NY 10019 United States
2. The Business of Data Science - New York, New York
3. Building your Data Science Toolbox, New York
4. NYC Summer Accelerator in Data Science & Analytics 2019, New York
5. Ethical Data Collection for Nonprofits, New York
6. Big Data Finance 2019, New York
7. Data Analysis and Linearization in Physics, New York
8. Intro To Python For Microsoft Excel & Data Analysis, New York
9. Machine Learning Immersive, New York
10. Dataware Hands-On Labs, New York
|1.||Chief Learning Officer Forum USA||March 7, 2017, to March 8, 2017|
730 3rd Ave New York NY 10017, USA
|2.||MLconf NYC: The Machine Learning Conference||March 24, 2017||230 Fifth Rooftop Bar|
|3.||Chief Data Officer, Financial Services||March 28, 2017 - March 29, 2017||New York, USA|
|4.||Marketing Metrics and Analytics Summit||April 26, 2017 - April 27, 2017||New York, USA|
|5.||Data Science Popup NYC||14 June, 2017||TBD|
|6.||O'Reilly Artificial Intelligence Conference||26 June, 2017 - 29 June, 2017||New York, USA|
|7.||2017 Sentiment Analysis Symposium, tackling the business value of sentiment, opinion, and emotion in our big data world||27 June, 2017 - 28 June, 2017|
New York Law School, 185 West Broadway, New York, NY 10013
|8.||12th International Conference on Mass Data Analysis of Images and Signals, MDA 2017||8 July, 2017 - 11 July, 2017||New York, USA|
|9.||17th Industrial Conference on Data Mining ICDM 2017||12 July, 2017 - 16 July, 2017||The Roosevelt New Orleans, A Waldorf Astoria Hotel|
|10.||13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM'2017||15 July, 2017 - 20 July, 2017||New York, USA|
|11.||JupyterCon, from Project Jupyter, the NumFOCUS Foundation, and O'Reilly Media||22 August, 2017 - 25 August, 2017|
New York Hilton Midtown 1335 Avenue of the Americas New York, New York, 10019
1. Chief Learning Officer Forum USA, New York
2. MLconf NYC: The Machine Learning Conference, New York
3. Chief Data Officer, Financial Services, New York
4. Marketing Metrics and Analytics Summit, New York
5. Data Science Popup NYC
6. O'Reilly Artificial Intelligence Conference, New York
7. 2017 Sentiment Analysis Symposium, tackling the business value of sentiment, opinion, and emotion in our big data world, New York
8. 12th International Conference on Mass Data Analysis of Images and Signals, MDA 2017, New York
9. 17th Industrial Conference on Data Mining ICDM 2017, New York
10. 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM'2017, New York
11. JupyterCon, from Project Jupyter, the NumFOCUS Foundation, and O'Reilly Media, New York
The ideal path to securing a job as a data scientist is as follows:
Getting started: Learning any programming language is the best way to start your journey as a data scientist. The most common programming languages are the R and Python programming. Having an idea of what data science is and what type of jobs it entails should be the first priority.
Mathematics: Data science is the study of data. It requires raw data to be stored, segregated and finally interpreted, which requires both mathematics and statistics. Having a good command over a few of the aspects of statistics can be quite helpful in data science, like:
Libraries: Data science is an advanced level of inventory making. Thus it not only preprocesses the data, but plots it as structured data and then uses AI algorithms on it to create databases. Some of the most popular libraries are:
Data Visualization: Having the presence of mind to categorize the raw data, finding similarities and being able to simplify the data for easy understanding is how you visualize the data. One of the popular forms is through graphs. There are various libraries you can use to make it easier for you:
Data preprocessing: Data scientists start with a large mass of data that needs to be preprocessed in order to be analysis ready. The preprocessing is done with feature engineering and variable selection. After this it is fed to ML tools for analysis.
Deep learning and ML: Machine Learning and deep learning are the mediums through which data is analyzed. The preprocessed data will work only with deep learning algorithms in order to analyze such a huge number of data. Both deep learning and ML are mandatory for your job application to be even considered. One should spend a few weeks reading up on CNN, RNN and neural networks.
Natural Language processing: One should have knowledge of NLP as it helps in analyzing text form of data and classifying them as well.
Polishing skills: There is no end to knowledge and competitions are a great way to brush up on your programming skills. Online platforms like Kaggle have opportunities to keep you working on your data science concepts. Outside online platforms you can make your own projects and study it individually.
Before you go for an interview as a Data Scientist, you must know the following ways to prepare before the day of the interview.
Study: Reread whatever you have learnt till now. There are few things you could brush up on:
Meetups and Conferences: Going to tech summits or developer meetups will acquaint you with the people who could one day become your colleagues. This is a good way to do some networking.
Competitions: Competitions are the best platforms to test your skills. Taking up projects to work on from Kaggle or GitHub would help polish your skills.
Referral: Having good referrals is considered one of the most important parts of a job interview. You should always keep your LinkedIn profile updated.
Know your Employer: Always research on the organization you are trying to get into. Having an idea of the type of company and values it has will give you a clearer perspective to your interview.
Interview: Once you feel that you are ready to attend an interview, go for it. Be comfortable and learn from your experience. Think of where you went wrong and how you could have answered the question that you were not prepared for during the interview.
Making inferences from data is the job of a data scientist. Finding patterns among structured and unstructured data, and analyzing them for the purpose of business growth will be a significant responsibility of a data scientist. In the era of virtual markets and job offerings there is a continuous flow of data that is structured and unstructured which can prove to be useful in making business decisions. The extraction of information that is appropriate for the industry will be done by data scientists.
Roles and Responsibilities of a Data Scientist are:
Data Science is the hottest job of 21st century and number one profession in 2019. Due to the high demand for data scientists and the limited number of experts in the field, data scientists earn at least 36% higher than predictive analytics professionals. The average salary for a Data Scientist is $130,070 per year in New York, NY.
A data scientist has the most unique position in a company. You will need to have an aptitude for mathematics, understand computer science and at the same time stay aware of current trends. 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 for a data scientist.
The following responsibilities are a part of a data scientist’s career graph:
There are various ways one can look for possible employees in New York:
Being the most popular career choice of 2019 there are various career opportunities for a Data Scientist-
Below are the key points on which every data scientist candidate is evaluated:
Data Science is a huge field which requires working with a large number of libraries. Finding the right programming language to master is, therefore, important.The most common languages are given below.
R programming: The only challenge in working with 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 are-
SQL: Working on relational databases, Structured Query Language has-
Java: One of the oldest programming languages, Java has limited libraries, which limits its potential. Nevertheless it has some advantages.
Scala: Your compiled Scala program can be run on JVM. It has 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 the 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:
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