Data Science with Python Training in New York, NY, United States

Get the ability to analyze data with Python using basic to advanced concepts

  • 40 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

Description

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.

What You Will Learn

Prerequisites

There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

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.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study

Hands-on:

  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

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.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing 

Hands-on:

Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

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.

Topics Covered:

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on: 

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

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.

Topics Covered:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on: 

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:  

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

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.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

 Project to be selected by candidates.

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

Projects

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

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).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science?

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:

  • Organizations are relying on data-driven decision making.
  • Since the need for well qualified Data scientists is disproportionately going up in relation to the number of professional Data scientists out there, the tech companies are paying high salaries to the qualified professionals in this field.
  • As the rate of data generation is increasing the need to analyze those data at an equally high rate is demanded. Data scientists can help companies make crucial decisions by providing their findings from segregating raw data.

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:

  • Python Coding
  • R Programming
  • Hadoop Platform
  • SQL database and coding
  • Machine Learning and Artificial Intelligence
  • Apache Spark
  • Data Visualization
  • Unstructured data
  1. Python Coding: Python is one of the simplest and most popular coding systems used by Data scientists. It is a versatile and easy to use programming tool that takes various formats of data and processes them. Python also helps in creating datasets and allows data scientists to perform operations on those datasets.
  1. R programming: The knowledge of at least one analytical tool is useful in taking forward your data science journey and becoming an expert. R programming is one such tool, knowledge of which will make any data science problem easier to solve.
  1. Hadoop Platform: This is an open source framework that processes and analyzes huge volumes of data. It is provided by Apache and is a major requirement for most data science jobs even though there are better alternatives available for it.
  1. SQL database and coding: SQL is the language in which data is created. Thus data scientists need to know this language in order to analyze, communicate as well as work on data and understand the structure and formation of a database. MySQL also possess concise commands to make operations on a database easier, while saving time and decreasing the number of technical skills needed by a data scientist.
  1. Machine Learning and Artificial Intelligence: It is mandatory to have proficiency in the subject of Machine Learning and Artificial Intelligence for professionals who want to pursue a career in data science. Some of the major concepts and knowledge of ML and AI that are necessary to learn are:
  • Reinforcement Learning
  • Neural Network
  • Adversarial learning 
  • Decision trees
  • Machine Learning algorithms
  • Logistic regression etc.
  1. Apache Spark: Apache Spark is a developed version of the Hadoop Platform. It is one of the most popular data computation and sharing platforms with the only difference from Hadoop being that Apache Spark is faster than Hadoop. While Hadoop reads and writes to the disk, Apache Spark creates caches of its computation in the system memory. Apache Spark is the effective way of running data science algorithms faster. It helps in the faster dissemination of processed data while making handling of unstructured data set easier. It also diminishes the loss of data and is a faster alternative making it easier for data scientists to carry out projects.
  1. Data Visualization: Visualization tools like d3.js, ggplot, Tableau and matplotlib help data scientists visualize data. Complex data obtained from performing processes on data sets needs to be transformed into easy formats that can be comprehended by data scientists. Data visualization enables organizations to work directly with the obtained data. The knowledge and outcome obtained from a particular data can be directly used on new data.

  1. Unstructured data: These are the contents that are not labelled and organized into database values like videos, social media posts, audio samples, customer reviews, blog posts etc. Data scientists need to work with such unstructured data.

Below are 5 behavioral traits needed to become a successful data scientist:

  • Always asking ‘why’: To work as a data scientist one needs to deal with a massive amount of data on a daily basis. For that one needs to have an insatiable hunger for knowledge to keep going.
  • Clarity of concept: Organized curiosity of what is going on around you and ‘why’ and ‘what’ you need to do is essential. Whether cleaning data or writing code, one should have clarity of thought of what needs to be done and why. 
  • Having a creative mind: Creativity is absolutely essential for any form of work, whether it is visualizing data, developing new tools or new modeling features. Being aware of what is missing and what needs to be done when faced with a problem is necessary to get the right results. 

  • Not getting carried away with our beliefs: One of the perils of creativity and a successful data scientist is getting carried away with your own creativity and confidence. Skepticism keeps a data scientist in check and rationalizes the creative mind to find the right solution and not get attached to its own invention. 

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.

  1. Highest paying job: Becoming a certified data scientist needs a lot of training and hard work, thus the pay has to be proportionate to the work put into it. There is a high demand for data scientists but a limited number of trained professionals out there. Hence data scientists get one of the highest salaries in the IT sector today.
  1. Great bonuses: Apart from the salary, data scientists get huge bonuses including equity shares and signing perks.
  1. Privilege of becoming an educator: Becoming a data scientist requires a lot of knowledge. Thus, by the time you become an expert you will probably have a Master’s or a PhD which will help you to receive offers to become a lecturer or a researcher at governmental as well as private institutions.
  1. Mobility: One of the greatest perks of being a data scientist is the freedom to work wherever, whenever. Working with a technology that is used in almost all the sectors provides data scientists the flexibility to work in any projects that interests you. You are not tied to your work and you can work in any industry of your choice.
  1. Networking: Being involved in the tech world by publishing research papers in international journals, attending conference will expand your interaction with people in the industry. You can get referrals from such networks.

  1. Security: Everyday there are new technologies coming up and disappearing without making any significant mark. This is not the case with data science. Data science is an ever evolving field which will always need professionals who know how to adapt and develop their own skills in various scenarios. Thus, any one with even the basic idea of data science will have job security in the long term.

Qualifications and Skill Sets of Data Scientists

  1. Analytic problem solving: To find a solution, you need to be aware of all the strategies and have a clear perspective to reach the right solution. 
  2. Communication Skills: Collecting data and analyzing it is not the only responsibility of a data scientist. Unless you can communicate the customer analytics or business strategies to companies then your job is only half done.
  3. Intellectual Curiosity: Having a thirst for knowledge and a constant need to ask ‘why’ leads a data scientist to have the right solutions. Unless you are driven by your work and are constantly curious about how to optimize an already perfect system, you cannot help your organization to evolve with the changing times.
  4. Industry knowledge: This is of great value if you want to be ahead of your competitors. Being up to date with the happenings in the industry will help you understand what needs your attention and what you can discard. Being aware of what your global competitors are thinking and adapting them to your work will make you an asset in any company; thereby bringing new opportunities. 

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:

  • Bootcamps: Bootcamps are the best way to improve your Python programming skills. Bootcamps are held for 1 to 2 weeks or for 4-6 months, offering both theoretical knowledge as well as hands-on experience. Bootcamps help you get a clear idea of different aspects of data science and at the same time train you for everything that you can expect in any data-driven enterprise.
  • MOOC courses: These are virtual courses and provide excellent knowledge of the latest trends in the industry. These courses are taught by experts helping you refine your implementation skills through assignments. They offer a mixture of both problem solving skills and theoretical skills that will be useful in any given scenario.
  • Certification: Certification is an important way of enhancing your skill set while also improving your CV. Some of the best data science certification courses are:
    • NYC Data Science Academy
    • Springboard
    • General Assembly
    • Thinkful
  • Projects: Projects are a great way to work on new solutions to problems that have been solved already depending on the restrictions of the projects. The more you work on projects, the better your analytical and problem solving skills will become.
  • Competitions: Attending competitions like Iron Viz or Kaggle, etc, improves your problem solving skills while giving you an idea of where you stand in relation to your peers. It also helps to optimize your rational analytic skills while staying within the given restraints, pushing your brain to work more efficiently in any situation.

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

  • Iris Data Set: Iris Data Set is the most engaging, adaptable, dynamic and comprehensive data set that you can begin your data science journey with. It has only 4 columns and 50 rows of data set that teaches you the various types of classification techniques. It is easy to understand and provides interesting insights into the more complicated aspects of data science. 
    • Practice Problem: The problem is using these parameters to predict the class of the flowers.
  • Loan Prediction Data Set: The domain of banking uses data analytics and data science methodologies as much as any other industry. The Loan Prediction Data Set introduces the concepts of data science that are applicable to banking and insurance. It has 13 columns and 615 rows of classification problem data set. The set includes the methods implemented in the detection of common variables that could provide insight into customer behavior and so on.
    • Practice Problem: Predict the probability of a bank to approve a given loan. 
  • Bigmart Sales Data Set: The retail store is one of the best places to study data collection, categorization and dissemination between retailers and customers. The use of data science is bound to be seen in this sector. Product placement, offer customization or inventory management are few of the things that are performed by using data science. The data set consists of 12 variables and 8523 rows used to determine Regression problems.
    • Practice Problem: Sales prediction of a retail store.

Intermediate level

  • Black Friday Data Set: This is another retail store data set where the sales trajectory of a retail store is analyzed. The data set explores and expands on feature engineering skills and understands daily transactions of hundreds of customers. This data set is a regression problem consisting of 550069 rows and 12 columns. 
    • Practice Problem: Predicting purchase amount. 
  • Trip History Data Set: This data set comes from a bike sharing service in the United States. Your pro data munging skills are tested in this data set. The quarter-wise data from 2010 is provided, with each file having 7 columns. This is a classification problem. 
    • Practice Problem: The class of users is required to be predicted.
  • Million Song DataSet: This is an interesting data set and can appeal to most people. It is based on the entertainment industry. This is a regression problem with 515345 observations and 90 variables which is not the complete database. This data set is a subset of the original data set of a million songs.
    • Practice Problem: Predicting the year of release of a song.

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.

    • Practice Problem: The problem depends on the image you have downloaded.
  • Chicago Crime Data Set: Since data science is all about working with data all organizations expect data scientists to work with large amounts of data. They no longer want to work with sample data when there are methods of computation of complete data sets. This data set is an important introduction to handling of large data sets using your local devices. While the problem is easy, the secret lies in your data management skills. This data set consists of 6 million observations making it a multi-classification problem.
    • Practice Problem: Prediction of the type of crime.

  • VisualQA Dataset: This is a test of your programming skills and intuition. The dataset contains open ended questions about images. To answer these questions one needs to have knowledge about computer vision and language. The dataset consists of 265016 images with 3 questions per images and 10 ground truth answer to every question. 
    • Practice Problem: Answer open-ended questions about images using deep learning technique.

How to Become a Data Scientist in New York

The following points will guide you to become a successful data scientist.

  1. Acquire basic programming skills: One of the first steps towards becoming a data scientist is to learn a programming language. Python and R programming are some of the most commonly used skills.
  2. Mathematics and statistics: As data science deals with data, having basic skills of algebra and statistics will make it easier to grasp the concepts of data science.
  3. Data visualization: The work of a data scientist is not just to understand data themselves but make it simple and coherent enough that non-experts can understand it perfectly. Visualization of data becomes an important aspect of data science as it is the end user who needs to understand the data generated more than the scientific aspect of data analysis. Having the ability to visualize patterns and common qualities will help the analyst to make sense of the data produced. 
  4. Deep Learning and ML: Having knowledge of deep learning and ML are a must for any data scientist. It is through the skills of deep learning and ML that data scientists analyze the data provided.

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:

  1. Get a degree: Data scientists are mostly Master’s or PhD degree holders. Hence, it is important to start preparing, reading and practicing as early as you can. You could get into numerous programs online or offline, or get yourself a degree in the basics of mathematics and algebra. Having a degree in computer science or statistics is also considered valuable. Thus, a certificate or degree course would be your first step. 
  2. Handling large quantity of data: Handling unstructured data is essentially the job of a data scientist. Knowing how to categorize the infinite number of data getting stored and making it cohesive is the most important responsibility as it's quite complex. Fitting the unstructured data into a database needs more than just technical skill, thus making it a trickier job. Working on data sets and projects can improve your ability to identify useful data. 
  3. Software and techniques to master: The softwares like Python, R and Hadoop are important tools. More than 53% data scientists are fluent in both R and Python programming.  Being accustomed to using these will kick-start your data science career:
    • R is a versatile programming language. It is easy to use and highly popular with most companies. Python is slowly catching up with R in terms of popularity and should also be learnt. 
    • Hadoop is a useful software especially when there are more data than there is storage. Hadoop quickly transfers the data to different points in the machine. Apache Spark is sometimes favored over Hadoop by many companies. It is similar in its computation work to Hadoop but is faster and a more reliable method of storing data. 
    • Understanding and collection are the preliminaries to interpreting data and classification of database. SQL queries are important to be learned for this reason.

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.

  • You have a strong STEM (Science/Technology/Engineering/Management) background: 0 points.
  • You have a weak STEM background (Biochemistry/Biology/Economics or other such degrees): 2 points. 
  • You come from a non-STEM background: 5 points
  • You have less than 1 year of experience working with Python programming: 3 points
  • You have never had a job which required you to code on a regular basis: 3 points
  • You feel you are not good at independent learning: 4points
  • You do not understand when it is said that this scorecard is a regression algorithm: 1 point.

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.

Data Scientist Salary in New York

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:

  1. Chance to work with a major brands as every renowned organization is entering the field of Data Science.
  2. Multiple job opportunities
  3. Job growth

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. 

Data Science Conferences in New York

S.NoConference nameDateVenue
1.Data Science Salon | NYC 2019June 13, 2019

VIACOM 1515 Broadway 2nd Floor New York, NY 10036 United States

2.The Business of Data Science - New York11 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

1. Data Science Salon | NYC 2019, New York

  • About the conference: The conference will bring together specialists to teach best Data Science practices used in Media and Entertainment.
  • Event Date: June 13, 2019
  • Venue: VIACOM 1515 Broadway 2nd Floor New York, NY 10036 United States
  • Days of Program: 1
  • Timings: 9:00 AM – 7:00 PM EDT
  • Purpose: The purpose of the conference is to apply Machine Learning and AI to Media and Entertainment.
  • How many speakers: 15
  • Speakers & Profile:  
    • Harini Kannan -  Data Scientist, Capsule8
    • Sergey Fogelson – Vice President, Data Science & Modeling, Viacom
    • Amy Yu – VP of Product Strategy and Data Science, Viacom
    • Anna Nicanorova – Director, Annalect Labs
    • Christopher Whitely – Senior Director, Applied Analytics, Comcast
    • Gilad Lotan – VP, Head of Data Science, Buzzfeed
    • Anna Coenen – Sr. Data Scientist, New York Times
  • Registration cost: $225 – $495
  • Who are the major sponsors: Viacom

2. The Business of Data Science - New York, New York

  • About the conference: The conference will teach you how your organization can benefit using Data Science and Artificial Intelligence.
  • Event Date: 11 June, 2019 to 12 June, 2019
  • Venue: Downtown Conference Center 157 William Street New York, NY 10038 United States
  • Days of Program: 2
  • Timings: Tue, Jun 11, 2019, 9:00 AM – Wed, Jun 12, 2019, 4:30 PM EDT
  • Purpose: The purpose of the conference is to teach the implementation of Data Science techniques in your organization.
  • Registration cost: $1,850 – $2,190
  • Who are the major sponsors: Pragmatic Institute

3. Building your Data Science Toolbox, New York

  • About the conference: The conference provides a platform for women in the field of Data Science to get together and discuss everyday Data Science.
  • Event Date: June 27, 2019
  • Venue: General Assembly 902 Broadway New York, New York 10010 United States
  • Days of Program: 1
  • Timings: 6:30 PM – 9:00 PM EDT
  • Purpose: The purpose of the conference is to help the women in the field of data science find a platform to discuss the latest trends in the data industry.
  • Registration cost: $5
  • Who are the major sponsors: Women in Data

 4. NYC Summer Accelerator in Data Science & Analytics 2019, New York

  • About the conference: This summer bootcamp is for students with an interest in Data Science and who want to have practical learning in one of the hottest sectors of the job market.
  • Event Date: 15 July, 2019 to 14 Aug, 2019
  • Venue: Midtown New York, NY 10036 United States
  • Days of Program: 30
  • Timings: Mon, Jul 15, 2019, 9:30 AM – Wed, Aug 14, 2019, 4:00 PM EDT
  • Purpose: The purpose of the bootcamp is to teach practical and marketable skills in the field of Data Science and analytics.
  • Whom can you Network in this Conference: You will be able to network with hiring managers from New York Times, American Express, McKinsey, Buzzfeed, and many more. You will have an opportunity to meet students with an interest in Data Science.
  • Registration cost: $3,305 – $3,888
  • Who are the major sponsors: Principal Analytics Prep

5Ethical Data Collection for Nonprofits, New York

  • About the conference: The conference will have a series of short exercises and case studies to introduce the attendees to Data collection and Internet of Things (IoT). These exercises will be tailor-made for nonprofit organizations.
  • Event Date: May 2, 2019
  • Venue: Civic Hall 118 W 22nd St 12th Floor New York, NY 10011 United States
  • Days of Program: 1
  • Timings: 1:00 PM – 5:00 PM EDT
  • Purpose: The purpose of the conference is to tackle the large-scale issues using Data Science.
  • How many speakers: 1
  • Speakers & Profile: Arlene Ducao, CEO and cofounder of Dukode's affiliate company Multimer
  • Registration cost: $30 – $60
  • Who are the major sponsors: Civic Hall

6. Big Data Finance 2019, New York

  • About the conference: The two-day conference will cover the latest developments in the field of Data Science of Finance.
  • Event Date: 9 May, 2019 to 10 May, 2019
  • Venue: Cornell Tech 2 West Loop Road New York, NY 10044 United States
  • Days of Program: 2
  • Timings: Thu, May 9, 2019, 8:30 AM – Fri, May 10, 2019, 5:00 PM EDT
  • Purpose: The purpose of the conference is to teach the latest mathematical techniques that can be used for data management.
  • Registration cost: $650
  • Who are the major sponsors: BigDataFinance.org

7. Data Analysis and Linearization in Physics, New York

  • About the conference: The conference will deal with the usage of linearization and data collection methods for students to help them with physics equations and principals. 
  • Event Date: 24 July, 2019 to 26 July, 2019
  • Venue: Teachers College, Columbia University 525 W 120th St Zankel Hall New York, NY 10027 United States
  • Days of Program: 3
  • Timings: Wed, Jul 24, 2019, 9:00 AM – Fri, Jul 26, 2019, 4:30 PM EDT
  • Purpose: The purpose of the conference is for students to get a clearer idea of what it is like to be a physicist by designing experiments, taking measurements and analyzing data.
  • Registration cost: $165 – $300
  • Who are the major sponsors: STEMteachersNYC

8. Intro To Python For Microsoft Excel & Data Analysis, New York

  • About the conference: In the conference, the attendees will be introduced to Python as a direct replacement for Microsoft Excel.
  • Event Date: May 8, 2019
  • Venue: Byte Academy 295 Madison Ave Fl 35 New York, NY 10017 United States
  • Days of Program: 1
  • Timings: 6:30 PM – 9:00 PM EDT
  • Purpose: The purpose of the conference is to determine the capabilities of Python in performing data analysis.  
  • Registration cost: $45 – $60
  • Who are the major sponsors: Byte Academy

9. Machine Learning Immersive, New York

  • About the conference: The conference aims to bring together Data Analysts, Data Scientists, and Machine Learning Engineers so that they can harness the benefits of predictive modeling and data-driven insights.
  • Event Date: 10 June, 2019 to 14 June, 2019
  • Venue: Practical Programming, 115 West 30th Street, 5th Floor, New York, NY 10001 United States
  • Days of Program: 5
  • Timings: Mon, Jun 10, 2019, 10:00 AM – Fri, Jun 14, 2019, 5:00 PM EDT
  • Purpose: The purpose of the conference is to understand the fundamentals of python, learning about exploratory data analysis, and putting projects on Github.
  • Registration cost: $999
  • Who are the major sponsors: Practical Programming ProgramWithUs.com

10. Dataware Hands-On Labs, New York

  • About the conference: The conference will provide an opportunity to the attendees to get a hands-on experience with the latest techniques and tools of Machine learning, ETL, data storage, analytics, visualization, streaming, etc.
  • Event Date: October 10, 2019
  • Venue: JW Marriott Essex 160 Central Park S New York, NY 10019 United States
  • Days of Program: 1
  • Timings: 6:00 PM – 8:00 PM EDT
  • Purpose: The purpose of the conference is to guide you through a series of exercises that will help you put the latest techniques into practice.
  • How many speakers:1
  • Speakers & Profile: Carol McDonald, Solutions Architect at MapR Technologies
  • Registration cost: $29.99
  • Who are the major sponsors: MapR Technologies
S.NoConference nameDateVenue
1.Chief Learning Officer Forum USAMarch 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

  • About the conference: It allowed its attendees to reach their goals effectively by networking them with highly experienced professionals from the areas of data science.
  • Event Date: March 7, 2017, to March 8, 2017
  • Venue: 730 3rd Ave New York NY 10017, USA
  • Timings: 08:00 AM - 06:00 PM (General) 
  • Purpose: The purpose of the conference was to provide its attendees with C-level learning and development view on data science.

2. MLconf NYC: The Machine Learning Conference, New York

  • About the conference: The conference discussed the latest research in machine learning techniques and practices, application of tools, algorithms, and platforms to solve the issues pertaining to data sets.
  • Event Date: March 24, 2017
  • Venue: 230 Fifth Rooftop Bar
  • Days of Program: 1
  • Purpose: Learned the latest trends in machine learning. 
  • How many speakers: 17
  • Speakers & Profile:
    • Aaron Roth, Associate Professor, University of Pennsylvania
    • Alexandra Johnson, Software Engineer, SigOpt 
    • Byron Galbraith, Chief Data Scientist, Talla
    • Corinna Cortes, Head of Research, Google
    • Erik Bernhardsson, CTO, Better Mortgage
    • Layla El Asri, Research Scientist, Maluuba
    • Ben Hamner - Co-Founder and CTO, Kaggle
    • Ben Lau - Quantitative Researcher, Complus Asset Management
    • Claudia Perlich - Senior Data Scientist, Two Sigma
  • Who were the major sponsors:

    3. Chief Data Officer, Financial Services, New York

    • About the conference: The conference allowed its attendees to achieve their goals effectively by connecting them to technologies, insights, and people.
    • Event Date: March 28, 2017 - March 29, 2017
    • Days of Program: 2
    • Timings: 09:00 AM-06:00 PM (expected)
    • Purpose: This conference connected over 100 leaders from the field of the data industry and imparted knowledge on best practices, latest innovations, and challenges.

    4. Marketing Metrics and Analytics Summit, New York

    • Event Date: April 26, 2017 - April  27, 2017
    • Purpose: The purpose of the conference was to discuss topics related to data science like real-time data-capturing, data siloing, machine learning and marketing automation.
    • How many speakers: 8
    • Speakers & Profile:
      • Peter Fader, Professor of Marketing, The Wharton School
      • Rob Armstrong, Data and Analytics Enthusiast, Teradata
      • Andrea Lopus Cardozo, Director of Consumer Insights, Pandora
      • Jeff Greenfield, Co-founder & COO, C3 Metrics
      • Eric Callhan, Director of Performance Analytics, American Express
      • Sakti Kunz, Head of Data and Analytics Solutions, Honeywell
      • John Guo, SVP, Head of Consumer and Commercial Modeling and Analytics, Fifth Third Bank
      • David Bakey, VP of Growth Marketing, Harry’s Grooming

      5. Data Science Popup NYC

      • About the conference: It provided a platform for researchers who are inquisitive and passionate for providing solutions to the challenges in data science, to discuss and learn the latest trends.
      • Event Date: 14 June, 2017
      • Days of Program: 1
      • Purpose: The goal of the conference was to develop best practices, share ideas and connect with others and provide real-time solutions to the challenges in the field of data science.

      6. O'Reilly Artificial Intelligence Conference, New York

      • About the conference: It brought together the world’s best innovators and researchers and leaders from top technology companies to present knowledge on hot topics such as machine learning, natural language, deep learning, AI and cloud along with a detailed discussion on tools, algorithms, and frameworks for a practical approach.
      • Event Date: 26 June, 2017 - 29 June, 2017
      • Purpose: The purpose was to bring together AI innovators and business leaders to discuss the developments and advancements in applied AI, across various fields.
      • How many speakers: 11
      • Speakers & Profile:
        • Richard Socher
        • Jim McHugh
        • Suchi Saria
        • Damion Heredia
        • David Ferrucci
        • Josh Tenenbaum
        • Doug Eck
        • Anca Dragan
        • Amy Unruh
        • Tuomas Sandholm
        • Naveen Rao

        7. 2017 Sentiment Analysis Symposium, tackling the business value of sentiment, opinion, and emotion in our big data world, New York

        • About the conference: This conference included speakers from companies like Uber, Youtube and innovative startups and tech providers to cover the business value of emotion, opinion, and intent.
        • Event Date: 27 June, 2017 - 28 June, 2017
        • Venue: New York Law School, 185 West Broadway, New York, NY 10013
        • Days of Program: 2
        • Purpose: The conference focused on advanced analytics through solutions, technology, and business presentations.
        • How many speakers: 49
        • Speakers & Profile:

          • Irene Aldridge, President, and Managing Director, Research, of AbleMarkets.com
          • Tom Anderson, founder, and managing partner of OdinText
          • Randi Barshack, co-founder of SAP 
          • Marija Bogic, Innovationbubble
          • Darren Bosik, lead,  Marina Maher Communications
          • Jared Broad, founder, and CEO of QuantConnect
          • Dr. Catherine Havasi, CEO and Co-Founder of Luminoso Technologies
        • Who were the major sponsors:
          • Zimgo
          • Crowdflower
          • Converseon
          • Socialgist
          • RealEyes

          8. 12th International Conference on Mass Data Analysis of Images and Signals, MDA 2017, New York

          • Event Date: 8 July, 2017 - 11 July, 2017
          • Purpose: The purpose of this conference was to connect researchers and discuss the automatic analysis of signals and images in various areas like chemistry, biotechnology, information robots, medicine, etc.

          9. 17th Industrial Conference on Data Mining ICDM 2017, New York

          • About the conference: The conference invited original research results and also promoted interaction and discussion on almost every aspect of data mining.
          • Event Date: 12 July, 2017 - 16 July, 2017
          • Venue: The Roosevelt New Orleans, A Waldorf Astoria Hotel
          • Days of Program: 5
          • Purpose: The purpose was to provide a platform for researchers and practitioners to interact, discuss and present their original ideas in future prospects and applications of data mining.
          • How many speakers: 3
          • Speakers & Profile:
            • Leslie Valiant - T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics, Harvard University
            • Michael J. Franklin - Liew Family Chair of Computer Science, University of Chicago
            • Aidong Zhang - Distinguished Professor, State University of New York at Buffalo

            10. 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM'2017, New York

            • Event Date: 15 July, 2017 - 20 July, 2017
            • Purpose: The conference provided a platform for researchers dealing in data mining and machine learning to discuss the latest trends and further advancements in the area.

            11. JupyterCon, from Project Jupyter, the NumFOCUS Foundation, and O'Reilly Media, New York

            • About the conference: It provided a platform for business analysts, researchers, tool creators, data scientists, educators, and project contributors to explore the project Jupyter platform.
            • Event Date: 22 August, 2017 - 25 August, 2017
            • Venue: New York Hilton Midtown, 1335 Avenue of the America, New York, New York, 10019 
            • Days of Program: 4
            • Purpose: The purpose of the conference was to explore and learn the best practices that can advance the workflow with Jupyter.
            • Who were the major sponsors:

              • NumFOCUS
              • O'Reilly Media
              • Anaconda Powered by Continuum Analytics
              • DataScience.com
              • Bloomberg
              • IBM
              • Domino Data Lab
              • Caserta Concepts
              • Two Sigma

            Data Scientist Jobs in New York

            The ideal path to securing a job as a data scientist is as follows:

            • Getting started
            • Mathematics
            • Libraries
            • Data visualization
            • Data processing
            • Machine Learning and deep learning
            • Natural language processing
            • Polishing skills

            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:

            • Descriptive statistics
            • Probability
            • Linear algebra
            • Inferential statistics

            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:

            • Sci-kit learn
            • SciPy
            • NumPy
            • Pandas
            • ggplot
            • matplotlib

            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:

            • matplotlib-Python
            • ggplot2-R

            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:

            • Probability
            • Statistics
            • Statistical models
            • Machine Learning
            • Understanding of neural networks.

            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:

            1. Classifying structured and unstructured data through pattern recognition and creating databases.
            2. Finding data that is relevant and profitable for the business from the vast numbers of data.
            3. Develop Machine Learning technologies, programs and tools which will help in the accurate analysis of the data.
            4. Statistical analysis of appropriate data for predicting future developments of a company is also expected of a data scientist.

            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:

            • Business Intelligence Analyst: Anyone in this position is expected to analyze the available data to understand the business and marketing trends of the industry his/her company is part of. This analysis of data is done to take the company forward by being clear about the position of a company in the business sector.
            • Data Mining Engineer: An engineer in data science has the task of analyzing data for the company as well as other third parties. Not only that, engineers are expected to optimize data analysis process by developing sophisticated algorithms.
            • Data Architect: A Data Architect’s work is to make the data sources more approachable. He or she works alongside developers, system designers to integrate and protect data while finding ways of centralizing, it making it more accessible.
            • Data Scientist: The data scientist works as an interpreter and ideator by working with sets of data that correspond with particular business ventures and predicts the efficacy by developing a hypothesis and comparing similar data. This is not all, a data scientist also develops algorithms and systems that will make analysis of data more simple and enable people to work directly with data.

            • Senior Data Scientist: A senior data scientist is expected to work with data in order to predict the future of a company. He or she should create projects and develop systems in the present with foresightedness so that the future conditions of a company can be predicted.

            There are various ways one can look for possible employees in New York:

            1. Through Data Science conferences
            2. Online platforms like LinkedIn
            3. Social gatherings like Meetup

            Being the most popular career choice of 2019 there are various career opportunities for a Data Scientist-

            1. Data Scientist
            2. Data Architect
            3. Data Administrator
            4. Data Analyst
            5. Business Analyst
            6. Marketing Analyst
            7. Data/Analytics Manager
            8. Business Intelligence Manager

            Below are the key points on which every data scientist candidate is evaluated: 

            1. Education: Since data science requires a sophisticated level of interpretation, having higher level education is always a criteria. Data scientists are considered to hold the most number of PhDs. Even getting certified can also help in landing the best job. .
            2. Programming: Programming is a crucial part of data science. Being well-versed in R and Python programming languages are a must for any data scientist.
            3. Machine Learning: It is ML and deep learning that analyzes data to find patterns and relationships after they have been prepared. Machine learning is imperative to any data science project.
            4. Projects: Companies look for data scientists who have hands-on experience. Unless you have practical knowledge of what you have learnt in theory, your education is not complete. Thus projects are a good way of providing an understanding of your capabilities while also adding value to your resume.

            Data Science with Python New York

            • Python is easy to use and comes with various libraries and packages that are useful to data scientists. It is structured and object oriented making it perfect for data science.
            • Python is the most simple and readable programming language that instantly attracts data scientists. It comes with appropriate analytic libraries and tools that are ideal for the kind of work done in data science. It is unique in relation to any other programming language making it the only choice for all data science projects.
            • The diversity of resources available on Python makes it a safe option for data scientists. If faced with a problem while working on a Python program or developing a data science model, data scientists have a wide variety of resources available to quickly work up a solution.

            • Another advantage of using Python is the availability of a community of developers using the same programming language. Python being the most popular programming language, the number of people working on it is high. Since the type of work being done with Python by data scientists is similar, if anyone faces difficulty at any point with their projects it is probable that someone else in the community had also faced the same problem and has already found a solution. Being part of a community with the most number of people makes it easier to brainstorm solutions.

            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.

            • It has a huge open-source community that provides numerous high quality open-source packages for R.
            • It boasts of smooth handling of matrix operations and has large statistical functions.
            • Included with it is ggplot2, that enables data visualization

            Python: With lesser packages than R, Python is still considered to be popular with data scientists. The reasons for that are-

            • Libraries like pandas, scikit-learn and tensorflow equip Python to provide most library needs for data science purposes.
            • It is very easy to use and operate.
            • It has an open-source community that is considered one of the largest.

            SQL: Working on relational databases, Structured Query Language has-

            • Readable syntax
            • Efficiency in updating, manipulating and querying data for relational databases.

            Java: One of the oldest programming languages, Java has limited libraries, which limits its potential. Nevertheless it has some advantages.

            • Systems that are coded with Java at the backend are more compatible with data science projects.
            • It is a high performance, general purpose, compiled language.

            Scala: Your compiled Scala program can be run on JVM. It has some advantages-

            • Running on JVM, Scala can run on Java as well.
            • Used alongside Apache Spark, it enables high performance computing cluster.

            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

            • Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party):

            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:

            • Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it: $ xcode-select --install
            • Install brew: Install Homebrew, a package manager for Apple, using following command: /usr/bin/ruby -e "$(curl -fsS https://raw.githubusercontent.com/Homebrew/install/master/install)" Confirm if it is installed by typing: brew doctor 
            • Install python 3: To install the latest version of python, use: 

             brew install python

            • To confirm its version, use: python --version

            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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            Trainer at KnowledgeHut made sure to address all my doubts clearly. I was really impressed with the training and I was able to learn a lot of new things. It was a great platform to learn.

            Meg Gomes casseres

            Database Administrator.
            Attended PMP® Certification workshop in May 2018
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            I feel Knowledgehut is one of the best training providers. Our trainer was a very knowledgeable person who cleared all our doubts with the best examples. He was kind and cooperative. The courseware was designed excellently covering all aspects. Initially, I just had a basic knowledge of the subject but now I know each and every aspect clearly and got a good job offer as well. Thanks to Knowledgehut.

            Archibold Corduas

            Senior Web Administrator
            Attended Agile and Scrum workshop in May 2018
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            Trainer really was helpful and completed the syllabus covering each and every concept with examples on time. Knowledgehut also got good customer support to handle people like me.

            Sherm Rimbach

            Senior Network Architect
            Attended Certified ScrumMaster®(CSM) workshop in May 2018

            FAQs

            The Course

            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.

            • Get advanced knowledge of data science and how to use them in real life business
            • Understand the statistics and probability of Data science
            • Get an understanding of data collection, data mining and machine learning
            • Learn tools like Python

            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

            • Python
            • MS Excel

            There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

            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. 

            Finance Related

            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.

            KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

            The Remote Experience

            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

            Have More Questions?

            Data Science with Python Certification Course in New York, NY

            Called a 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 perhaps, New York, which is the nerve center of the US. It 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, its John F. Kennedy International Airport is one of the busiest airfields in the world. The city is where the famous New York Stock Exchange is based. It is also the biggest printing and publishing center in the country. Renowned for its theater, you can catch a Broadway play in its theater district. There is no place more dynamic than New York. Professionals who wish 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. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.