Data Science with Python Training in New Jersey, NJ, United States

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.
  • Create robust predictive models with advanced statistics.
  • Leverage hypothesis testing and inferential statistics for sound decision-making.
  • 250,000 + Professionals Trained
  • 55,000 + Programmers upskilled
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Grow your Data Science skills

This four-week course takes you from the fundamentals of Data Science to an advanced level. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 42 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 Advanced Learning

  • Code Reviews by Professionals

Why Become a Data Scientist?

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Exclusive Post-Training Sessions

Practical one-to-one guidance from mentors: project review and evaluation, guidance on work challenges.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be useful.

Who should attend this course?

Anyone interested in the field of data science

Anyone looking for a more robust, structured Python learning program

Anyone looking to use Python for effective analysis of large datasets

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

1

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

2

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

3

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

4

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

5

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

6

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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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.


Topics

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

Learning objectives

The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives

In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

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

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives

Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

  • Analysis of Variance (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 for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives

Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression & classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

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

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives

All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

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

Topics

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

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing 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.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

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

  • Project to be selected by candidates.

Frequently Asked Questions

Data Science with Python Training

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:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

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:

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

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

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

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.

Data Science with Python Workshop

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.

What Learners Are Saying

Ong Chu Feng

Ong Chu Feng

Data Analyst

4/5

The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to un View More

Attended Data Science with Python Certification workshop in January 2020

Elyssa Taber

Elyssa Taber

IT Manager.

3/5

I would like to thank the KnowledgeHut team for the overall experience. My trainer was fantastic. Trainers at KnowledgeHut are well experienced and really helpful. They completed the syllabus on time, and also View More

Attended Agile and Scrum workshop in June 2020

Rosabelle Artuso

Rosabelle Artuso

.NET Developer

5/5

The course which I took from Knowledgehut was very useful and helped me to achieve my goal. The course was designed with advanced concepts and the tasks during the course given by the trainer helped me to step View More

Attended PMP® Certification workshop in August 2020

Vito Dapice

Vito Dapice

Data Quality Manager

5/5

The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts. Now, I am in the process of completing the certification. Overall good View More

Attended PMP® Certification workshop in April 2020

Jules Furno

Jules Furno

Cloud Software and Network Engineer

5/5

Everything from the course structure to the trainer and training venue was excellent. The curriculum was extensive and gave me a full understanding of the topic. This training has been a very good investment fo View More

Attended Certified ScrumMaster (CSM)® workshop in June 2020

Raina Moura

Raina Moura

Network Administrator.

5/5

I would like to extend my appreciation for the support given throughout the training. My special thanks to the trainer for his dedication, and leading us through a difficult topic. KnowledgeHut is a great place View More

Attended Agile and Scrum workshop in January 2020

Ellsworth Bock

Ellsworth Bock

Senior System Architect

5/5

It is always great to talk about Knowledgehut. I liked the way they supported me until I got certified. I would like to extend my appreciation for the support given throughout the training. My trainer was very View More

Attended Certified ScrumMaster (CSM)® workshop in February 2020

Hillie Takata

Hillie Takata

Senior Systems Software Enginee

5/5

The course material was designed very well. It was one of the best workshops I have ever attended in my career. Knowledgehut is a great place to learn new skills. The certificate I received after my course help View More

Attended Agile and Scrum workshop in August 2020

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Data Science with Python

What is Data Science

The Data Scientist job is in the highest demand nowadays and a Data Scientist is the No. 1 job in New Jersey. The job has enormous openings with base salaries of about $102,116. The job is said to have a healthy trend in the long run. In New Jersey, corporations like Fidelity Investments, Jet.com, Audible, Wiley, Charles River Associates, Source Enterprises, Daugherty Business Solutions, etc. employData Scientists.

The popularity of data science is mainly because :

  • There is more requirement of decisions based on data
  • There is a shortage of well-trained data scientists. As such, good data scientists get lucrative salaries
  • Since data is being collected at an extremely high rate, there is a need to make the most out of the collected data. Data Scientists are responsible for doing exactly that. 

New Jersey is the home of several institutions that provide Master’s degree in Data Science including New Jersey Institute of Technology, Rowan University, Rutgers University, Saint Peter’s University, Stevens Institute of Technology, Thomas Edison State University, etc.

To become a data scientist, the following skills are a necessity:

  1. Coding in Python: Python is popularly used in the field of data science. The programming language is relatively simple and offers a lot of versatility. It can take different formats of data and perform different operations on them. It allows creation of datasets and various operations on these datasets
  2. R Programming: If you want to become a data scientist, you need to have a good knowledge of at least one analytical tool. Knowing R programming is always advantageous in the field of data science
  3. Hadoop: While Hadoop platform isn’t necessarily a requirement in the field of data science, but it is still highly preferable. It is one of the major skill requirements for data science jobs.
  4. DBMS and coding in SQL: The language of SQL is specifically designed for aiding data scientists work on data. It helps them gain insights into formation and structure of database. With the help of MySQL commands, database operations can be performed quickly even with lower level of technical skills. 
  5. AI and Machine Learning: For a successful career in data science, having sufficient knowledge of Machine Learning and AI is required. Any potential data scientist should be familiar with the following Machine Learning and AI concepts:
    • Neural Network
    • Reinforcement Learning
    • Decision trees
    • Adversarial learning 
    • Logistic regression 
    • Machine Learning algorithms
  6. Apache Spark: When it comes to data sharing technologies, Apache Spark is one of the most popular ones. Similar to Hadoop, it is used for computation of big data. The difference lies in the fact that it is comparatively faster. This is because it uses cache memory instead of reading and writing to the disk. Data scientists use Apache Spark for running data science algorithms faster. It also aids in handling of large as well as complex and unstructured datasets. It is fast, easy to use and prevents loss of data, which allows projects to be carried out efficiently.
  7. Data Visualization: A data scientist should be able to visualize data using tools like d3.js, ggplot, Tableau and matplotlib. Using these tools, data scientists can convert the results obtained through processes performed on data sets into a format that can be easily understood and comprehended. With the help of data visualization, organizations can work on data directly. Data scientists can also grasp insights from particular data and the outcome from it. 
  8. Unstructured Data: Unstructured data is the content that isn’t labelled and organized into database. Data scientists should be able to work with such data, which include videos, customer reviews, blog posts, audio samples, etc. 

A successful data scientist should have the following behavioural traits:

  • Clarity: Data Science isn’t the field for those who can’t find clarity in things. From writing code to cleaning up data, you need to have a clear idea of everything you would be doing. 
  • Curiosity: The job involves dealing with lots of data on a daily basis, so you need to be eager to learn to keep going. 
  • Creativity: In terms of creativity, there is a lot of scope in data science like finding innovation in the ways of visualizing data and developing new modelling features or tools. 
  • Scepticism: Data Scientists need to keep their creativity in check through appropriate scepticism.

Owing to the earning potential, career opportunities rating, and job openings, Data Science has become the No.1 job in the tech world. Corporations like Bank of America, Morgan Stanley, Dow Jones, Liquidnet, Deloitte, JP Morgan Chase, Citi, BirlaSoft, Novartis, Primesys Technologies, TRANZACT, Goldman Sachs, etc. are hiring data scientists to join their team.

Considering the job of a data scientists has been described as the “Sexiest job of the 21st century”, there are definitely certain benefits associated with it, including:

  • Lucrative Pay: Data scientists are in high demand and low supply, which is why it is one of the highest paying jobs nowadays. 
  • Bonuses: Apart from the pay, data scientist get excellent bonuses as well along with other perks like signing and equity shares. 
  • Knowledge: There is a high demand for knowledge in data science. You are likely to have a Masters or a PhD when you become a data scientist. With such qualifications, you can even work as a researcher or lecturer
  • Location of work: Most businesses that collect data are located in developed countries. You can get a job with high salary and have great standard of living. 
  • Networking: Through conferences, international journals and other platforms, you can build a network of data scientists.

Data Scientist Skills and Qualifications

Following the top business skills you must have to become data scientist:

  • Problem Solving: You need to be aware of the strategies and be able to think clearly for analysing a problem and understanding it to solve it.
  • Communicative skills: A key role for a data scientist is to communicate deep business and customer analytics to companies
  • Curiosity: You should have some curiosity to thrive in the field of data science
  • Industry knowledge: Probably one of the most crucial skills is knowledge of the industry, which provides better perspective on what is more important.

Following are the ways of polishing your skills for data scientist jobs:

  • MOOC Courses: Online courses can be found on all the latest data science trends. With expert teachers and assignments, it is a great way to improve skills
  • Boot camps: Bootcamps offer comprehensive practical experience along with theoretical understanding. They serve as an excellent way to get your basics covered.
  • Projects: Projects provide you constraints under which you need to find solutions to questions which have already been answered. It can help you refine your skills
  • Certifications: Certifications are not only good for your CV, but also for your overall skills. Some famous certifications in data science are:
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Competitions: Problem solving skills can also be improved through participation in online competitions. Kaggle is an example of such competition.

Many organizations have adopted Data Science to apply big data analytics. There are several corporations in New Jersey that are looking for Data Scientists to help them make sense of the data like Fidelity Investments, Jet.com, Audible, Wiley, Charles River Associates, Source Enterprises, Daugherty Business Solutions, Bank of America, Morgan Stanley, Dow Jones, Liquidnet, Deloitte, JP Morgan Chase, Citi, BirlaSoft, Novartis, Primesys Technologies, TRANZACT, Goldman Sachs, etc.  

If you want to improve your Data Science skills, the best way to do so is to practice solving problems related to Data Science. Depending on the difficulty level of problem you are comfortable with, you can practice the following data science problems:

Beginner Level

  • Iris Data Set: For pattern recognition, Iris Data Set is considered to be highly resourceful and versatile. It is easy to incorporate to learn the various classification techniques. For beginners in the field of data science, it is the best data set. It contains 50 rows along with 4 columns.
    • Practice Problem:  To predict the class of a flower according to the parameters.
  • Bigmart Sales Data Set: The Retail Sector uses analytics heavily for optimizing business processes. Business Analytics and Data Science allow efficient handling of operations. The data set contains 8523 rows and 12 variables and is used in Regression problems.
    • Problem to Practice: Predicting a retail store’s sales
  • Loan Prediction Data Set: As compared to all other industries, the banking field uses data science and analytics most significantly. This data set can help a learner by providing an idea of the concepts in the field of insurance and banking, along with the strategies, challenges and variables influencing outcomes. It contains 615 rows and 13 columns.
    • Problem to Practice: Predicting whether a given loan would be approved by the bank or not.

Intermediate Level:

  • Black Friday Data Set: This Data Set consists of retail store’s sales transaction and it can be used for exploring and expanding engineering skills. It is a regression problem and contains 550,609 rows and 12 columns.
    • Problem to practice: Predicting the total purchase amount
  • Text Mining Data Set: This data set contains safety reports describing problems encountered on flights. It is a multi-classification and high-dimensional problem and contains 21,519 columns and 30,438 rows.
    • Problem to practice: Classifying documents depending on their labels.
  • Human Activity Recognition Data Set: This Data Set consists of 30 human subjects collected through smartphone recordings. It consists of 10,299 rows and 561 columns.
    • Problem to practice: Predicting the category of human activity.

Advanced Level:

  • Urban Sound Classification: Most beginner Machine Learning problems do not deal with real world scenarios. The Urban Sound Classification introduces ML concepts for implementing solutions to real world problems. The data set contains 8,732 urban sound clipping classified in 10 categories. The problem introduces concept of real-world audio processing.
    • Problem to practice: Classifying the type of sound from a specific audio.
  • Identify the digits: This data set contains 31 MB of 7000 images in total, each having 28x28 dimension. It promotes study, analysis and recognition of image elements.
    • Problem to practice: Identifying the digits in an image.
  • Vox Celebrity Data Set: Audio processing is an important field in the domain of Deep Learning. This data set contains words spoken by celebrities and is used for speaker identification on a large scale. It consists of 100,000 words from 1,251 celebrities from across the world.
    • Problem to practice: Identifying the celebrity based on a given voice.

How to Become a Data Scientist in New Jersey

Given below are the steps needed to become a top data scientist:

  1. Select an appropriate programming language to begin with. R and Python are usually recommended
  2. Dealing with data involves making patterns and finding relationship between data. A good knowledge of statistics and basic algebra is a must
  3. Learning data visualization is one of the most crucial steps. You need to learn to make data as simple as possible for the non-technical audience
  4. Having the necessary skills in Machine Learning and Deep Learning is necessary for all data scientists.

To prepare for a data science career, you need to follow the given steps and incorporate the appropriate skills:

  1. Certification: You can start with a fundamental course to cover the basics. Thereafter, you can grow your career by learning application of modern tools. Also, most Data Scientists have PhDs, so you will be required to have the right qualifications.
  2. Unstructured data: Raw data is not used in the database as it is unstructured. Data scientists have to understand the data and manipulate it to make it structured and useful.
  3. Frameworks and Software: Data scientists need to know how to use the major frameworks and software along with appropriate programming language.
    • R programming is preferred because it is widely used for solving statistical programs. Even though it has a steep learning curve, 43% data scientists use R for data analysis.
    • When the amount of data is much more than the available memory, a framework like Hadoop and Spark is used.
    • Apart from the knowledge of framework and programming language, having an understanding of databases is required as well. Data scientists should know SQL queries well enough.
  4. Deep Learning and Machine Learning: Deep Learning is used to deal with data that has been gathered and prepared for better analysis.
  5. Data Visualization: Data Scientists have the responsibility or helping business take informed decisions through analysis and visualization of data. Tools like ggplot2, matplotlib, etc. can be used to make sense of huge amounts of data.

New Jersey has several institutions like New Jersey Institute of Technology, Rowan University, Rutgers University, Saint Peter’s University, Stevens Institute of Technology, Thomas Edison State University, etc. that provide a Master’s degree program in Data Science. The course will help you understand the concepts of Data Science and acquire all the skills required to become a top-notch data scientist.

According to a study revealed, 46% of data scientists have a PhD, with 88% of all data scientists having a Master’s degree. The importance of degree in the field is summarized below:

  • Networking: Networking is important in all fields and it can be developed while pursuing degrees.
  • Structured education: Having a structured curriculum and a schedule to follow is always beneficial.
  • Internships: These allow much needed practical experience
  • Qualification for CVs: Earning a degree from a reputed institution is always helpful for your career.

If you want to study Data Science in New Jersey, there are several institutions that offer a postgraduate program in Data Science. But first, you need to figure out if you need a Data Science degree or not. The given scorecard can help you determine whether you should get a Master’s degree. You should pursue the degree if you get over 6 points in total:

  • A strong background in STEM (Science/Technology/Engineering/Management)- 0 point.
  • Weak STEM background, such as biochemistry, biology, economics, etc.- 2 points
  • Non-STEM background- 5 points
  • Python programming experience less than 1 year in total- 3 points
  • No job experience in coding- 3 points
  • Lack of capability to learn independently- 4 points
  • Not understanding that this scorecard that follows a regression algorithm- 1 point.

Programming knowledge is a must for any aspiring data scientist because:

  • Analysing data sets: Programming helps data scientists to analyse large amounts of data sets
  • Statistics: The knowledge of statistics is not enough. Knowing programming is required to implement the statistical knowledge.
  • Framework: The ability to code allows data scientists to efficiently perform data science operations. It also allows them to build frameworks that organizations can use for visualizing data, analysing experiments and managing data pipeline.

Data Scientist Salary in New Jersey

The average salary of New Jersey based Data Scientist is $100,450 per annum. 

In comparison to New York, the average data scientist salary in New Jersey is $734 more. 

In New Jersey, the average annual salary of a Data Scientist is $100,450. On the other hand, in Boston, the average annual salary is $125,310. 

In Chicago the average annual salary of a Data Scientist is $110,925. On the other hand, in New Jersey, the average annual salary is $100,450. 

There is a high demand for data scientists in New Jersey owing to the several firms looking forward to using Data Science while making important business decisions.

Here are the benefits of being a Data Scientist in New Jersey:

  • Better income
  • Career growth
  • Job opportunities

Data Scientists have an important role to play in an organization. That offers them certain perks and advantage over others. Not only do they get an opportunity to connect with top-level executives but they also get an opportunity to work in their preferred field. Today, data science has spread its roots in all the fields allowing data scientists to select a field they are actually interested in.

Some of the companies hiring Data Scientists in New Jersey include Comrise, Hackensack Meridian Health and RCI. 

Data Science Conferences in New Jersey

Conference nameDateVenue
Central NJ Data Science Meetup
Saturday, May 18, 2019
Monmouth Junction, NJ
New Jersey Data Science Meetup
Saturday, May 18, 2019
Parsippany-Troy Hills Library

1. Central NJ Data Science Meetup, New Jersey

  • About the New Jersey conference: This conference takes place once a month and covers all the topics of data science. This is open for beginners as well as experienced. 
  • Event Date: Saturday, May 18, 2019
  • Venue: Monmouth Junction, NJ
  • Days of Program: 1
  • Timings: 10:30 AM to 1:00 PM
  • Registration cost: $2.00/per person

2. New Jersey Data Science Meetup, New Jersey

  • About the New Jersey conference: Organized by Northwestern Alumni, this event is for Data Science, Predictive Analytics, Machine Learning, Data Analytics, Business Analytics, BI, BIG Data Professionals and those who are interested in learning Analytics.
  • Event Date: Saturday, May 18, 2019
  • Venue: Parsippany-Troy Hills Library
  • Days of Program: 1
  • Timings: 2:00 PM to 4:00 PM
  • Registration cost: $3.00 /per person
Conference nameDateVenue
NJ Edge Conference
11-12 January, 2018
Whippany, New Jersey
 CIO Conference
11-12 January, 2018

NJ Tech Council 96 Albany Street, New Brunswick, NJ 08901

1. NJ Edge Conference, New Jersey

  • About the conference: The conference discussed best practices and innovations in online learning tools and technologies, also in cybersecurity and Big Data.
  • Date: 11-12, January 2018
  • Venue: Whippany, New Jersey
  • Purpose: The conference focused on digital transformation, cybersecurity, and Big Data through demonstrations and presentations.

2. CIO Conference, New Jersey

  • About the conference: This conference focused on how companies and enterprises implement digital transformation to accelerate business growth 
  • Date: 4 October, 2017
  • Venue: NJ Tech Council 96 Albany Street, New Brunswick, NJ 08901
  • Days of the program: 1
  • Purpose: The conference aimed to address strategies involving compliance, liability, security operations, and IT.

Data Scientist Jobs in New Jersey

Logically, the following step sequence needs to be followed for getting a Data Scientist job:

  1. Initial Step: Start by knowing the fundamentals of data science along with the role of a data scientist. Select a programming language, preferably R or Python.
  2. Mathematical understanding: Since data science involves making sense of data by finding patterns and relationships between them, you need to have a good grasp of statistics and mathematics, particularly topics like:
    • Descriptive statistics
    • Linear algebra
    • Probability
    • Inferential statistics
  3. Libraries: The process of data science involves tasks like pre-processing data, plotting structured data and application of ML algorithms. The popular libraries include:
    • SciPy
    • Scikit-learn
    • Pandas
    • NumPy
    • Matplotlib
    • ggplot2
  4. Visualizing data: Data scientists need to find patterns in data and make it simple for making sense out of it. Data visualization is popularly done through graphs and libraries used for that include ggplot2 and matplotlib.
  5. Data pre-processing: Pre-processing of data is done with the help of variable selection and feature engineering to convert the data into a structured form so that it can be analysed by ML tools.
  6. Deep Learning and ML: Along with ML, knowledge of deep learning is preferable since these algorithms help in dealing with huge data sets. You should take time learning topics such as neural networks, RNN and CNN.
  7. NLP: All data scientists are required to have expertise in Natural Language Processing, which involves processing and classification of text data form.
  8. Brushing up on skills: You can take your skills to the next level by taking part in competitions such as Kaggle. You can also work on your own projects to polish your skills.

The steps given below can help you improve your chances of getting data scientist jobs:

  • As a part of interview preparation cover the important topics such as:
    • Statistics
    • Probability
    • Statistical models
    • Understanding of neural networks
    • Machine Learning
  • You can build and expand your network and connections through data science meetups and conferences
  • Participation in online competitions can help you test your own skills
  • Referrals can be helpful for getting data science interviews, so you should keep your LinkedIn profile updated.
  • Finally, once you think you are ready, go for the interview.

The profession of data scientist involves discovery of patterns and inference of information from huge amounts of data, for meeting goals of a business.

Nowadays, data is being generated at a rapid rate, which has made the data scientist job even more important. The data can be used for discovering ideas and patterns that can potentially help advance businesses. A data scientist has to extract information out of data and make relevant sense out of it for benefitting the business.

Roles and responsibilities of data scientists:

  • Fetching relevant data from structured and unstructured data
  • Organizing and analyzing the extracted data
  • Making sense of data through ML techniques, tools and programs
  • Statistically analyzing data and predicting future outcomes

As compared to other professionals in predictive analytics, data scientists have 36% higher base salary. The average salary for a Data Scientist is $102,116 per year in New Jersey.

A data scientist can spot trends and use mathematics and computer science skills. Data scientists have to decipher and analyse big data and make future predictions accordingly.

A data science career path can be explained through the following roles:

  • Business Intelligence Analyst: This role requires figuring out the trends in the business and the market. It is done through data analysis.
  • Data Mining Engineer: The job of a Data Mining Engineer is to examine data for business as well as a third party. He/she also has to create algorithms for aiding the data analysis.
  • Data Architect: Data Architects work with users and system designers and developers for creating blueprints used by DBMS for integrating, protecting, centralizing and maintaining data sources.
  • Data Scientist: Data Scientists per analysis of data and develop a hypothesis by understanding data and exploring its patterns. Thereafter, they develop systems and algorithms for productive use of data for the interest of business.
  • Senior Data Scientist: The role of Senior Data Scientists is anticipating future business needs and accordingly, shaping the present project, data analyses and systems.

Following are the top professional organizations for data scientists in New Jersey:

  • Data Science + FinTech
  • Jersey City Raspberry Pi Ardiuno Computer Hardware Hangout
  • UX-Data
  • M Science Women in Data

Apart from referrals, other effective ways of networking with data scientists in New Jersey include:

  • Online platforms such as LinkedIn
  • Data Science Conferences
  • Meetups and other social gatherings

There are numerous career options in the field of data science, including:

  • Data Scientist
  • Data Analyst
  • Data Architect
  • Marketing Analyst
  • Business Analyst
  • Data Administrator
  • Business Intelligence Manager
  • Data/Analytics Manager

Some key points that employers look for while employing data scientists include:

  •   Qualification and Certification: Having high qualification is a must and certain certifications also help
  •   Python: Python programming is highly used and is usually preferred by companies
  •   Machine Learning: It is an absolute must to possess ML skills
  •   Projects: Working on real world projects not only helps you learn data science but also build your portfolio as someone capable of handling challenging projects

Data Science with Python New Jersey

  • Multi-paradigm programming language: Python involves numerous packages and libraries suited for Data Science purposes.
  • Simple and Readable: It is highly preferred by data scientists over other programming languages due to its simplicity and the dedicated packages and libraries made particularly for data science use.
  • Diverse resources: Python gives data scientists access to a broad range of resources, which helps them solve problems that may come up during the development of a Python program or Data Science model.
  • Vast community: The community for Python is one of its biggest advantages. Numerous developers use Python every day. So, a developer can get help from other developers for resolving his/her own problems,and the community is highly active and generally helpful.

The field of data science is huge involving numerous libraries and it is important to choose a relevant programming language.

  • R: It offers various advantages, even though the learning curve of the language is steep.
    • Huge open source community with high quality packages
    • Availability of statistical functions and smooth handling of matrix operations
    • Data visualization tool through ggplot2
  • Python: It is one of the most popular languages in data science, even though it has fewer packages in comparison to R.
    • Easier learning and implementation
    • Huge open-source community
    • Libraries required for the purpose of data science are provided through Panda, tensorflow and scikit-learn
  • SQL: This structured query language works on relational database
    • The syntax is readable
    • Allows efficient updation, manipulation and querying of data.
  • Java: It doesn’t not have that many libraries for the purpose of data science. Even though its potential is limited, it offers benefits like:
    • Integrating data science projects is easier since the systems are already coded in Java
    • It is a compiled and general-purpose language offering high performance
  • Scala: Running on JVM, Scala has complex syntax, yet it has certain uses in the field of data science.
    • Since it runs of JVM, programs written in Scala are compatible with Java too
    • High performance cluster computer is achieved when Apache Spark is used with Scala.

Data Science with Python Certification Course in New Jersey, NJ

A view at a map of the United States will tell you that New Jersey is one of the smallest states. But did you know that it is the most thickly populated state in the union? A state that was the site of several decisive battles during the American Revolutionary War, New Jersey has come a long way. Today is one of the most progressive, well defined places in terms of high-tech and banking headquarters. A vibrant place, New Jersey is surrounded on the southeast and south by the Atlantic Ocean, it borders on the north and east by New York State, on the west by Pennsylvania, and on the southwest by Delaware. Interestingly, the first organized baseball game was played in Hoboken, NJ in 1846. It has the highest number of horses per square mile than any other state. This amazing city is full of opportunities for those armed with the right credentials. KnowledgeHut helps you with this by offering a range of courses to choose from including-- PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, Big Data Analysis, Apache Hadoop, and many more.

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