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Take KnowledgHut’s hands-on comprehensive Data Science with Python course in New York and build a robust foundation in Data Science. Go from beginner to advanced level in weeks with hands-on programming experience in Python. Prepare yourself with the skills to work with substantial data and discover precious insights into real-world business implications.
..... Read more42 Hours of Live Instructor-Led Sessions
60 Hours of Assignments and MCQs
36 Hours of Hands-On Practice
6 Real-World Live Projects
Fundamentals to an Advanced Level
Code Reviews by Professionals
The demand for data engineers in New York has been rapidly growing. Worldwide, the demand for data engineers was up by 50% and the demand for data scientists was up by 32% in 2020. This is a substantial increase which is only going to follow an upward trend in the coming years. So, capitalize on the demand for the hottest job of the 21st century.
..... Read moreNot sure how to get started? Let our Learning Advisor help you.
Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.
Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.
Get trained by leading practitioners who share best practices from their experience across industries.
Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.
Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!
Six months of post-training mentor guidance to overcome challenges in your Data Science career.
Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.
Lambda function and the object-oriented way of writing classes and objects.
Importing datasets into Python, writing outputs and data analysis using Pandas library.
Data values, data distribution, conditional probability, and hypothesis testing.
Analysis of variance, linear regression, model building, dimensionality reduction techniques.
Evaluation of model parameters, model performance, and classification problems.
Time Series data, its components and tools.
Learning objectives
Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.
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Learning objectives
The Python module will equip you with a wide range of Python skills. You will learn to:
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Hands-on
Learning objectives
In the Probability and Statistics module you will learn:
Topics
Hands-on
Learning objectives
Explore the various approaches to predictive modelling and dive deep into advanced statistics:
Topics
Hands-on
Learning objectives
Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:
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Hands-on
Learning objectives
All you need to know to work with time series data with practical case studies and hands-on exercises. You will:
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Hands-on
Learning objectives
This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.
Hands-on
The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:
The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.
Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.
In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.
The Data Science with Python course is ideal for:
There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.
To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -
Hardware requirements
Software Requirements
System Requirements
On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.
In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.
By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.
The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.
Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.
The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science.
The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.
Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.
Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years.
Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.
Schedules for our upcoming workshops in Data Science with Python can be found here.
We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.
You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.
Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.
In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.
If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.
Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.
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:
Beginner Level
Intermediate level
Advance 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.
| S.No | Conference name | Date | Venue |
| 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
| S.No | Conference name | Date | Venue |
| 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 --version
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.
About New York
Called the land of opportunity, there is no place in the world like New York. A city of immigrants, it is one of the cultural centers of the Western World. No other city attracts more tourists than New York, the nerve center of the US. New York is a frontrunner in manufacturing, commerce, foreign trade, and banking, book and magazine publishing, and theatrical production.
Besides being the most important seaport in the region, New York houses the John F. Kennedy International Airport, one of the busiest airfields in the world. The city is also where the famous New York Stock Exchange and the biggest printing and publishing center in the country is based. Renowned for its theater, you can catch a Broadway play in its theater district.
Data Science with Python Training in New York
Professionals who wish to reach new heights in their career can find immense opportunities with certifications such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, and others.
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