Data Science with Python Training in New Jersey, NJ, 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
Group Discount

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.

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

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.

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.

reviews on our popular courses

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KnowledgeHut Course was designed with all the basic and advanced concepts. My trainer was very knowledgeable and liked the way of teaching. Various concepts and tasks during the workshops given by the trainer helped me to enhance my career. I also liked the way the customer support handled, they helped me throughout the process.

Nathaniel Sherman

Hardware Engineer.
Attended PMP® Certification workshop in May 2018
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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 up in my career. I loved the way the technical and sales team handled everything. The course I took is worth the money.

Rosabelle Artuso

.NET Developer
Attended PMP® Certification workshop in May 2018
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I am glad to have attended KnowledgeHut’s training program. Really I should thank my friend for referring me here. I was impressed with the trainer, explained advanced concepts deeply with better examples. Everything was well organized. I would like to refer some of their courses to my peers as well.

Rubetta Pai

Front End Developer
Attended PMP® Certification workshop in May 2018
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It’s my time to thank one of my colleagues for referring Knowledgehut for the training. Really it was worth investing in the course. The customer support was very interactive. The trainer took a practical session which is supporting me in my daily work. I learned many things in that session, to be honest, the overall experience was incredible!

Astrid Corduas

Senior Web Administrator
Attended PMP® Certification workshop in May 2018
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I would like to extend my appreciation for the support given throughout the training. My special thanks to the trainer for his dedication, learned many things from him. KnowledgeHut is a great place to learn and earn new skills.

Raina Moura

Network Administrator.
Attended Agile and Scrum workshop in May 2018
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I had enrolled for the course last week. I liked the way KnowledgeHut framed the course structure. The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts.

York Bollani

Computer Systems Analyst.
Attended Agile and Scrum workshop in May 2018
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The course material was designed very well. It was one of the best workshops I have ever seen in my career. Knowledgehut is a great place to learn and earn new skills. The certificate which I have received after my course helped me get a great job offer. Totally, the training session was worth investing.

Hillie Takata

Senior Systems Software Enginee
Attended Agile and Scrum workshop in May 2018
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I am really happy with the trainer because the training session went beyond expectation. Trainer has got in-depth knowledge and excellent communication skills. This training actually made me prepared for my future projects.

Rafaello Heiland

Prinicipal Consultant
Attended Agile and Scrum 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 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.