Data Science Course with Python in Boston, MA, United States

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

  • Master Data Science fundamentals with real-world case studies and assignments 
  • Learn to extract crucial business insights using data analysis and visualization techniques 
  • Model predictive model performance using various metrics like recall, specificity and precision
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
  • 100 + Countries and counting

Grow your Data Science skills

Our comprehensive course will help you learn all you need to about Data Science and its techniques, beginning from the basics up to the advanced level. You will develop a strong foundation through real-world case studies and assignments. You'll also learn to extract valuable business insights using data analysis and visualization tools.

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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 an Advanced Level

  • Code Reviews by Professionals

Why get the Data Science with Python certification in Boston

data-science-with-python-certification-training

The demand for data engineers in Boston is increasing rapidly. Did you know that the demand for data engineers was up by a whopping 50% in 2020, compared to 2019? On a similar scale, the demand for data scientists was up by 32% in 2020 compared to the previous year. The average salary for a data scientist in the U.S. is $96,494 per year.

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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.

Continual Learning Support

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

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course in Boston 
  • Elementary programming knowledge will be of advantage 

Who should attend the Data Science with Python course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules for Boston

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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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Data Science with Python Course Curriculum

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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 and 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.

FAQs on Data Science with Python Course in Boston

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.

Additional FAQs on Data Science with Python Training in Boston

What is Data Science

Data scientists are professionals who analyze and manage huge amounts of data. Data Science has evolved as one of the most promising careers today. In today’s date, skilled data scientists are a requirement for every company and industry. The demand for data scientists is growing every year with a predicted rate of 28% growth in demand by 2020. Data has been used for decision making by many organizations in Boston, MA including Vectra, C.R. Bard, Digitas, BD, Localytics, JOYN BIO, Amazon, Spotify, Beacon Health Options, Trianz, Wayfair, etc.

The reasons for the popularity of Data Science as a career choice are as follows:

  • Huge demand: Skilled data scientists are very valuable to companies since they possess the skills to identify relevant questions, organize the information, collect data from a multitude of different data sources, translate results into solutions, and communicate their findings in a way that affects business decisions in a positive and healthy way. They deliver appropriate results to various stakeholders across an organization or business. Hence, because of the dependencies of the companies on them, there is a huge demand for data scientists in the market.
  • Low supply: This technology has a long way to go, and it is not going to fade out in the near future owing to the great advancements made continuously. Hence, we can say that it is still in a budding phase. Highly skilled professionals are required since most of the data handled by them are sensitive and confidential. Therefore, companies cannot afford to compromise with the security and handling of this data. This has led to a low supply of skilled data scientists with respect to the demands and requirements of the organizations.

Fun: Besides its financial and economic aspects, data science comes with many entertaining aspects. It is perfect for people who are curious about new things for it gives a wide scope of creativity and learning. It’s still unexplored, and the more one dives into it, the more new doors will open for him. The huge impact this technology has gives you the freedom to lead your imagination in any direction. Any area, you name it, and data science has the capacity to revolutionize it in a miraculous way.

The top skills that are needed to become a data scientist include the following:

  1. Programming/Software
  2. Artificial Intelligence
  3. Statistics/ Mathematics
  4. Machine Learning
  5. Data Cleaning
  6. Data Munging
  7. Data Visualization
  8. Unstructured data

When it comes to Master’s degree in Data Science, Boston, MA has several colleges that will help you get a degree. These colleges include University of Massachusetts, Boston University, and Harvard University.

  1. Programming/Software: Programming skills can help you to integrate with several data analytics seamlessly. Proficiency in software packages is the top skill necessary to be possessed by the data scientists to extract, clean, analyze, and visualize data efficiently. Therefore, a knowledgeable programmer could play vital roles in the prescriptive, descriptive, experimental and theoretical fields of data science. Some of the technical programming skills like Python coding, R programming language, JavaScript and HTML, Perl, or C/C++, Spreadsheet tools (such as Excel) and SQL Coding have come to stay in the field of data analysis. Programming complements data analytics by increasing the breadth and depth of data.
  2. Artificial Intelligence: Artificial Intelligence is basically a computer that is able to mimic or simulate human thought or behavior by analyzing, and using the data available. The AI platforms simulate the behavior of human minds like problem-solving, social intelligence, general intelligence, and learning. The various AI platforms available are as follows:
    • Google Cloud Prediction API
    • TensorFlow
    • MindMeld
    • Wipro HOLMES
    • Wit
    • Rainbird
    • Microsoft Azure Machine Learning
  3. Statistics: A concrete understanding of multivariable calculus and linear algebra is essential for a data scientist. Examples of Statistical Learning problems include:
    • Identify the risk factors for prostate cancer.
    • Predict whether someone will have a heart attack on the basis of demographic, diet and clinical measurements.
    • Establish the relationship between salary and demographic variables in population survey data.
    • Customize an email spam detection system.
  4. Machine Learning: Machine Learning is a subset of Artificial Intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on making predictions from data available from past experiences. Some machine learning methods are as follows:
    • Supervised machine learning algorithms
    • unsupervised machine learning algorithms
    • Semi-supervised machine learning algorithms
    • Reinforcement machine learning algorithms
  5. Data Cleaning: Filtering and modifying your data such that it is easier to explore, understand, and model is called data cleaning. Sometimes, some value is missing in data sets. Similar issues can arise like an error in data types, duplicates, and irrelevant information might be present in the datasets which should be removed. It is very important that the data is correct and accurate before data scientists work on it. Therefore, a considerable amount of time and effort is spent to ensure this, using the following tools. 
    • Trifacta, 
    • OpenRefine, 
    • Paxata, 
    • Alteryx, 
    • Data Ladder, 
    • WinPure
  6. Data Munging: All the activities that you do on the raw data to make it "clean" enough to input to your analytical algorithm are termed as data munging. The main aspect of data munging is mapping data from its raw form to another format with the purpose of making it more valuable and appropriate for advanced tasks such as Data Analytics and Machine Learning. The goal of data munging is to reduce the time spent on collecting and arranging the data.
    • Tabula
    • OpenRefine
    • DataWrangler
    • CSVKit
    • Python and Pandas
    • Mr. Data Converter
    • “R” packages
  7. Data visualization: Data visualization methods are an important part of analytics and refer to the creation of graphical representations of information by utilizing complex sets of numerical or factual figures. The essential data visualization techniques are as follows:
    • Know Your Audience
    • Set Your Goals
    • Choose The Right Chart Type
    • Number charts
    • Maps
    • Pie charts
    • Gauge charts
    • Take Advantage Of Color Theory
    • Handle Your Big Data
    • Use Ordering, Layout, And Hierarchy To Prioritize
    • Utilize Word Clouds And Network Diagrams
    • Include Comparisons
  8. Unstructured Data: Unstructured data refers to data that does not follow any order like spreadsheet pages, database tables or other linear or ordered data sets. Non-textual unstructured data such as MP3 audio files, JPEG images, and Flash video files, etc. and textual data including Word documents, PowerPoint presentations, instant messages, collaboration software, documents, books, social media posts, and medical records are all examples of unstructured data.

Below are the top 5 behavioral traits of a successful Data Scientist -

  • Critical Thinking –  A data scientist needs to go beyond identifying and analyzing a problem. He/she must be able to solve them as well. They should be able to apply the objective analysis of facts on a given issue or problem before formulating opinions or driving conclusions. This demands them to be critical thinkers. They should be able to assess a problem or situation from multiple points of view.
  • Team player  – Good data scientists can’t stay in their own domains. They should be willing to work with people who come from different points of view. A team has a group of members with different skills, someone strong in technology with someone strong in analytics, boosted with someone strong in business knowledge and someone with a broader view of the latest research and development in academia and industry. Hence, it is important to uphold the team spirit and professionalism to gain fast success. 
  • Hard skills and soft skills Apart from the technological skills required for Data Science, it is also important to have an open mind. It is easy for them to pick up on new programming trends in the ever-changing space. Since this technology is evolving day by day, people should be accepting and always ready to learn and pick up the pace in order to not get left behind.
  • Communication skills – Data scientists should be able to communicate with multiple stakeholders using data. Communication in this field includes communicating about the business benefits of data to business executives; about technology and computational resources; about the challenges with data quality, privacy, and confidentiality; and about other areas of interest to the organization. These should be elaborated and explained.

A Harvard Business Review article labeled “data scientist” as the sexiest job of the 21st century. There are several organizations that use data science for business optimization in Boston, MA including Celect, Vertex Pharmaceuticals, Klaviyo, Homesite Insurance, Altisource, The Boston Consulting Group, etc.Some of its benefits can be summarised as follows:

  1. Handsome payoff: Data Science is the largest arena in the technology sector with about 41% of the total in-demand workforce outlook. The average salary for a Data Scientist in Boston, MA is $125,310. In addition to technology, work in the corporate setting, marketing, consulting, healthcare, and financial services is also significant. 
  2. Proper training and certification: Certified data scientists who have undergone meticulous training can enjoy two major benefits: Their path to promotion is shorter, and chances of resume shortlisting are very high. There is also a wide scope for recruitment and growth for the self-taught data scientists as well. Many courses offered in data science are created by experts with concrete knowledge and experience in the field. Data scientists with certification in related fields of data science can expect around 58% pay raise, while the non-certified professionals get only 35%.
  3. A safe career to pursue: In the IT culture, every tool and software stays in the market for a while and then is replaced by a different alternative or an upgraded version. This has made most of the roles unstable in IT industry because the employees need to keep pace with the upcoming technologies. This leaves people with no option but to learn new technologies within a short span of time because the demands of the companies also change accordingly. Many who fail to catch up with the changing trends are even forced to switch jobs leading to instability in their career. Data science promises to stay in demand for a long time as most of its aspects are yet to be explored. There are a lot of opportunities in this career. According to reports, the amount of work for data architects is massive; the estimated growth over the next decade is 3% higher than the estimated growth for all other fields of work.
  4. Freedom to work: Data Science is an ever-evolving technology. New applications and inventions are added to it every day. There is a great scope for more possibilities. This requires you to think in a creative and innovative way. The best part of this field is its limitlessness, so you can enjoy the freedom of your imagination.
  5. Network: Due to the ever-increasing progress in the field of data science, many conferences and workshops are organized from time to time across the globe. These workshops invite notable expertise from various industries to share their ideas and knowledge with others on the latest and upcoming innovations. It is a golden opportunity for a data scientist to connect with other people from this field and create a strong network.

Data Skills - Boston

Below is the list of top business skills needed to become a data scientist: 

  1. Analytic Problem-Solving
  2. Communication Skills
  3. Business acumen
  4. Curiosity
  1. Analytic Problem-Solving – You must be able to apply your problem-solving skills to derive a conclusion. Decision making requires critical thinking and analytical solutions. Problem-solving skills are required for the following:
    • To apply a useful framework to solve a business problem
    • To use linear regression to generate business insights
    • To determine which analytical method to apply given the nature of the problem and available data
  2. Communication Skills – Data insights are usually presented in the form of tables, charts, or any other concise forms to be easily understood. A data scientist should have good communication skills to translate the ideas in a way understandable by other people. Using a storytelling approach to explain ideas makes it easier to understand and narrate to the team.
  3. Business acumen It is important to understand the business requirements of the organization you are working with, on a broader scale. You must understand how your business operates and how these techniques will be applied in real time so that your solutions fit accordingly. It also helps to categorize the problems on the grounds of priority.
  4. Curiosity – A data scientist should always be inquisitive and should know when to ask questions. He/ She should always be ready to learn new things and accept challenges. Mediocrity might cause a problem, as a data scientist, you should strive to stand out.

Below are the best ways to brush up your data science skills for data scientist jobs:

  • Boot camps: Boot camps focus on teaching topics that are relevant and intensive. There are many roles available in data science. Depending on which role you desire, you can take up specific boot camps. Here is the list of top boot camps in Data Science in Boston, MA:
    •  Science to Data Science
    • The Data Incubators
    • Ubiqum Code Academy
    • DataCamp
    • Data Science Dojo
    • General Assembly
    • RMOTR
    • NYC Data Science Academy
  • Online courses: There are many Data Science courses available online to take up at your convenience. Here is a list of few top online courses available:
    • Data Science Certification from Harvard University (edX)
    • Data Science and Statistics Certification by MIT (edX)
    • Applied Data Science with Python Certification (University of Michigan)
    • Microsoft Professional Program in Data Science (edX)
    • Data Science Certification from John Hopkins (Coursera)
    • Statistics and Data Science MicroMasters — MIT (edX)
    • CS109 Data Science — Harvard
    • Python for Data Science and Machine Learning Bootcamp — Udemy
  • Certifications: Data science is vital to nearly every company and industry, but the skills that recruiters are looking for will vary across businesses and industries. To add specific skills demanded by your desired industry, you can take up certifications. Here is the list of top certifications available to choose from:
    • Dell Technologies Data Scientist Associate (DCA-DS)
    • Data Science Council of America (DASCA)
    • Certified Analytics Professional (CAP)
    • Cloudera Certified Professional: CCP Data Engineer
    • Applied AI with DeepLearning, IBM Watson IoT Data Science Certificate
    • SAS Certified Advanced Analytics Professional
    • Microsoft Professional Program in Data Science
    • HDP Data Science
    • IBM Certified Data Architect
    • Cloudera Certified Associate - Data Analyst
  • Projects and Research papers: Mini or major projects in data science provide you a practical approach to how things work in real life. You get an opportunity to apply your theoretical knowledge in real time and understand how this technology impacts the industry. Here is the list of 5 topics that you can take up for your project:

    • Data Cleaning
    • Exploratory Data Analysis
    • Machine Learning
    • Interactive Data Visualization
    • Communication
  • Competitions: You can test your knowledge by participating in the competitions being organized in Data Science. It will help you to judge yourself and understand where you stand. It provides a great opportunity for learning. It provides you an exposure to state of the art approaches and datasets.

Data science has become an important aspect of all kinds of companies and organizations. In Boston, MA, there are several such organizations that hire data scientists like Boston, MA including Celect, Vertex Pharmaceuticals, Klaviyo, Homesite Insurance, Altisource, The Boston Consulting Group, Vectra, C.R. Bard, Digitas, BD, Localytics, JOYN BIO, Amazon, Spotify, Beacon Health Options, Trianz, Wayfair, etc.

To start working on Data Sets and to practice your Data Science skills, you can take up projects to work on. You can categorize it into the following three levels:

  • Beginner Level: This level comprises of data sets which are simple and are very easy to work with. They don’t require complex data science techniques. You can solve them using basic techniques like regression or classification algorithms. There are sufficient tutorials available online to go through these datasets. Here is a list of such data sets:
    • Iris Data
    • Bigmart Sales Data
    • Loan Prediction Data
    • Heights and Weights Data
    • Boston Housing Data
    • Wine Quality Data
    • Time Series Analysis Data
  • Intermediate Level: This level comprises of data sets which are medium to large in size and require more challenging efforts to solve them. These datasets demand some advanced pattern recognition skills. Here is the list of such datasets:
    • Movie Lens Data
    • Human Activity Recognition Data
    • Black Friday Data
    • Trip History Data
    • Siam Competition Data
    • Twitter Classification Data
    • Million Song Data
    • Census Income Data
  • Advanced Level: This level includes datasets that require the knowledge in advanced topics like neural networks, deep learning, recommender systems, etc. It also features high dimensional datasets. The datasets included in this level are as follows:

    • Urban Sound Classification
    • Age Detection of Indian Actors Data
    • Recommendation Engine Data
    • Vox Celebrity Data
    • Identify your Digits
    • Chicago Crime Data
    • ImageNet Data

Learn Data Science in Boston

Below are the right steps to becoming a successful data scientist:

  1. Choose an academic path: A Bachelor’s degree in Computer Science or a related field is important. More and more data scientists are opting for master's degrees. But this depends on the role offered by the company. So you must choose your academic path accordingly.
  2. Mathematics and statistics: A concrete understanding of multivariable calculus and linear algebra is a must for a data scientist. It forms the basis of many data analysis techniques. It is important for data scientists to be proficient in math since it simplifies writing algorithms. 
  3. Fundamentals: Before diving into the depths of Data Science, you must start with the fundamental concepts. You must master the basics to build a strong foundation for future learning. Below are some of the fundamentals that you can incorporate:
    • Start with statistics
    • Assess your assumptions
    • Suitable sampling
    • Data Engineering
    • Solid programming skills
  4. Specializations: Many data scientists will be heavily specialized in business, often specific segments of the economy or business-related fields like marketing or pricing. The need of the companies varies according to their objectives. You must choose a career path to follow and get proficient in one or more technologies related to data scientists. You can go for certifications, boot camps, and online courses. Having worked on related projects will help you upgrade your profile.
  5. Apply for jobs: There are several openings every now and then by companies for various posts related to data science. You need to keep a check on these notifications and prepare yourselves for the interviews.

Here are a few key steps and skills required that will help you get started in the field of Data Science:

  1. Degree/certificate: Get a degree or a certification in Data Science. You can either go for online or offline course, depends on what suits you. You will be learning about the new, cutting-edge technology that will give your Data Science career a tremendous boost.
  2. Unstructured data: Whatever data is provided to a data scientist, it is mostly in the raw form, meaning that is unlabelled and cannot be fit into a database. This makes the job of a data scientist much more complex. They have to understand this unlabeled and unstructured data so that they can manipulate it to get results.
  3. Software and Frameworks: If you are looking to work on Data Science projects, you will have to learn how to use the software and framework associated with it. You also need the knowledge of a programming language like Python or R.
    • R, one of the most commonly used programming languages in the field of Data Science for statistical problems, has a steep learning curve. About 43% of data scientists use R for their data analysis.
    • Hadoop is one of the most popular frameworks used by Data Scientists. When the amount of data is much more than the machine can handle, Hadoop comes into play. It works by conveying the data to different points on the machine. Another popular framework used by data scientists is Spark. It is faster than Hadoop. It also prevents loss of data which can happen in Hadoop.
    • Next, you move to databases. You need to learn how to write queries in SQL.
  4. Machine learning and Deep Learning: After you have gathered the data and structured it, you need to start applying algorithms to it to begin the analysis. Deep learning techniques are used to analyze the data and train the model. 
  5. Data Visualization: It is the responsibility of a data scientist to perform data visualization and making informed decisions using the analysis of the data. Graphs and charts are used to visualize the data. Tools like matplotlib, ggplot2, etc. are used to perform data visualization.

There are several institutions in Boston, MA offering a Master’s in Data Science. These colleges include University of Massachusetts, Boston University, and Harvard University.  Having a degree in Data Science is very important if you want to get employed as a Data Scientist in Boston, MA. About 88% of data scientists have a Master's degree while about 46% have a Ph.D. degree.

A degree is very important because of the following – 

  • Networking – During the course, you will make friends and acquaintances that will help you build your professional network.
  • Structured learning – You will be following a schedule and keeping up with the curriculum that will be beneficial and effective for your learning.
  • Internships – An in-office internship will help you get the practical hands-on experience required for getting a job in the field of data science.

  • Recognized academic qualifications for your résumé – A degree will look good on your CV and will put you ahead of other candidates.

Grade yourself on the basis of the below scorecard to check if you need a Master’s degree in Data Science or not. If your score is more than 6 points, we recommend a Master’s degree:

  • A strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • A weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • A non-STEM background: 5 points
  • Less than 1 year of experience in Python: 3 points
  • No experience of a job that requires regular coding: 3 points
  • Independent learning is not your cup of tea: 4 points
  • Cannot understand that this scorecard is a regression algorithm: 1 point

Knowledge of programming is perhaps the most important and fundamental skill that an aspiring data scientist must possess. Some of the other reasons why knowledge in programming is required include the following: 

  • Data sets: Data sets are basically a collection of data. Algorithms are written to work on these data sets, therefore it is very essential to have a command over one or more programming languages. Some of these programming languages are as follows:
    • R
    • Python
    • Scala
    • Julia
    • TensorFlow
    • Java
  • Statistics: Statistics is important for Data analysis. To recognize a pattern and work on them requires a good knowledge of statistics. A concrete understanding of multivariable calculus and linear algebra is essential for a data scientist.
  • Framework: The most recommended framework for Data Science is Hadoop which is an open-source software framework and is heavily preferred in several data science projects for processing of large data sets. One important feature of it is can store unstructured data such as text, images, and video. The benefits of Hadoop are features like flexibility, scalability, fault tolerance, and low cost which makes it a preferable choice for data scientists.

Data Science Salary - Boston

The average salary of a Data Scientist in Boston is $125,310 per year.

The data scientists earn an average of $125,310 in Boston as compared to $110,925 in Chicago.

A data scientist earns an average of about $125,310 every year in Chicago as compared to $128,623 earned by a Data Scientist in New York.

In Boston, the average annual salary of a Data Scientist is $125,310. On the other hand, in Washington, the average annual salary is $122,328. 

Data Scientists earn an average of about $125,310 in Boston as compared to $106,976 in Worcester. 

The average earning of a data scientist, about $125,310 every year, in Boston is higher than given to a data scientist in Worcester, which is $84,992.

Data Scientists earns an average of about $125,310 in Boston as compared to $100,449 in Springfield.

All the major organizations in the world are producing data. There is so much data but few skilled people to make sense of it. Boston is in dire need of data scientists who can bring out actionable business insights from raw numbers.

The benefit of being a Data Scientist in Boston is that it is a rapidly evolving field. If you are working as a Data Scientist in Boston, you will get an opportunity to work on new ideas and techniques. Also, your exploration and enthusiasm will be rewarded through exciting projects.

Data Scientists are in top demand right now. The advantage of being a Data Scientist in a city like Boston is the job growth the city offers. With so many companies looking for data scientists, there are multiple options available. This includes small companies as well as big ones. Also, it is easy for a data scientist to connect with top level management as they are the ones involved in converting raw data into useful business insights. A Data Scientist gets to work with the latest technology in a field of their interest.

The Data Science companies with job openings in Boston are Kensho Technologies, Klaviyo, DataRobot, TVision, Agero, Localytics, SHYFT Analytics, GNS Healthcare, Charles River Analytics, Kevmi, True Fit, nToggle, InsightSquared, RapidMiner, etc.

Data Conferences - Boston

S.NoConference nameDateVenue
1.The Data Science Conference®23 May, 2019 to 24 May, 2019

Hyatt Regency Boston 1 Avenue de Lafayette Boston, MA 02111 United States

2.ODSC East 2019 - Open Data Science Conference30 Apr, 2019 to 3 May, 2019

Hynes Convention Center 900 Boylston St Boston, MA 02115 United States

3.Postgres Vision 201924 June, 2019 to 26 June, 2019Sheraton Boston Hotel 39 Dalton St Boston, MA 02199 United States
4.Accelerate AI Summit, ODSC East 201930 Apr, 2019 to 1 May, 2019

Hynes Convention Center 900 Boylston St Boston, MA 02115 United States

5.2 Days Seminar Current regulatory thinking on Data Integrity
9 July, 2019 – 10 July, 2019

Hilton Garden Inn Boston Logan Airport 100 Boardman Street, Boston Massachusetts, Boston MA 02128, Boston MA 02128

United States

1. The Data Science Conference®, Boston

  • About the conference: This conference is a meeting space for Data Scientists just for learning and discussing Data Science, Machine Learning, Data Mining, Big Data, Artificial Intelligence, and Predictive Modeling without any vendor, sponsor or recruiter.
  • Event Date: 23 May, 2019 to 24 May, 2019
  • Venue: Hyatt Regency Boston 1 Avenue de Lafayette Boston, MA 02111 United States
  • Days of Program: 2
  • Timings: 7 AM to 5:30 PM
  • Purpose: The purpose of the conference is to attend a Data Science event without getting prospected by any other Data Science professional.
  • Registration cost: $1,250
  • Who are the major sponsors: The Data Science Conference®

2. ODSC East 2019 - Open Data Science Conference, Boston

  • About the conference: The conference will have the best and the most influential Data Scientists, innovators, and practitioners together to discuss Artificial Intelligence, Data Science and Big Data.
  • Event Date: 30 Apr, 2019 to 3 May, 2019
  • Venue: Hynes Convention Center 900 Boylston St Boston, MA 02115 United States
  • Days of Program: 5
  • Timings: 9 AM to 6 PM EDT
  • Purpose: The purpose of the conference is to provide technical skills, hands-on training and boost the professional profile of the attendees.
  • Registration cost: $521.11 – $2,221.11
  • Who are the major sponsors: ODSC Team | odsc.com

3. Postgres Vision 2019, Boston

  • About the conference: The conference will cover open source data management and the future of enterprise Postgres.
  • Event Date: 24 June, 2019 to 26 June, 2019
  • Venue: Sheraton Boston Hotel 39 Dalton St Boston, MA 02199 United States
  • Days of Program: 3
  • Timings: Mon, Jun 24, 2019, 6:00 PM – Wed, Jun 26, 2019, 5:00 PM EDT
  • Purpose: The purpose of the conference is to help the data science professionals learn about the Postgres and its relation with the open source data management.
  • Registration cost: $186.05 – $610.22

4. Accelerate AI Summit, ODSC East 2019, Boston

  • About the conference: The conference will help the top industry executives and managers learn how their business can be transformed with AI and Data Science.
  • Event Date: 30 Apr, 2019 to 1 May, 2019
  • Venue: Hynes Convention Center 900 Boylston St Boston, MA 02115 United States
  • Days of Program: 3
  • Timings: Tue, Apr 30, 2019, 9:00 AM –Wed, May 1, 2019, 6:00 PM EDT
  • Purpose: The purpose of the conference is to learn about the different Data Science and AI techniques to prepare the company for the upcoming wave of innovation.
  • Registration cost: $99 – $2,221.11
  • Who are the major sponsors: ODSC Team | odsc.com

5. 2 Days Seminar Current regulatory thinking on Data Integrity, Boston

  • About the conference: The seminar focuses on Data Integrity and cyber security. We will also go through the evolving trends in these fields to help our attendees stay on top of their fields.
  • Event Date: 9 July, 2019 – 10 July, 2019
  • Venue: Hilton Garden Inn Boston Logan Airport 100 Boardman Street, Boston Massachusetts, Boston MA 02128, Boston MA 02128 United States
  • Days of Program: 2
  • Timings: Tue, Jul 9, 2019, 8:30 AM – Wed, Jul 10, 2019, 4:00 PM EDT
  • Purpose: The purpose of the seminar is to understand what data integrity really means and how to navigate through different Data Integrity guidelines.
  • Registration cost: $1,295 – $3,995
  • Who are the major sponsors: worldcomplianceseminars
S.NoConference nameDateVenue
1.Data Science Conference, BostonMay 23-24, 2018

Hyatt Regency Boston, One Avenue de Lafayette, Boston, Massachusetts, USA, 02111

2.Data Science Meetup, ODSC East 2018Wednesday, May 2, 2018Boston Convention Center, 415 Summer Street · Boston, MA
3.Deep Learning for Robotics Summit, 201823 - 24th May, 2018510 Atlantic Avenue, InterContinental, Boston
4.Deep Learning in Healthcare Summit
25 - 26 May 2017
Renaissance Boston Waterfront Hotel, 606 Congress Street, Boston, Massachusetts, 02210

1. Data Science Conference, Boston

  • Conference City: Boston, USA 
  • About conference: The conference established a common stage for sponsor-free, vendor-free and recruiter-free meeting pool for data scientists.
  • Event Date: May 23-24, 2018
  • Venue: Hyatt Regency Boston, One Avenue de Lafayette, Boston, Massachusetts, USA, 02111
  • Days of Program: Two
  • Timings: 7:00 PM onwards 
  • Purpose: The conference served as a meeting pool for professionals from various fields like big data, data mining, data science, predictive modeling, machine learning and artificial intelligence.
  • Registration cost: $1,250

2. Data Science Meetup, ODSC East 2018, Bosto

  • Conference City: Boston, USA
  • About: The conference discussed Data Science, Data Optimism and creating Robust Data Science Capabilities in an Asset Management Firm.
  • Event Date: Wednesday, May 2, 2018
  • Venue: Boston Convention Center, 415 Summer Street · Boston, MA
  • Days of Program: One
  • Timings: 6:15 PM to 8:15 PM
  • Purpose: The Annual Data Science Meetup brought together professionals to share their developments in Data Analysis. 
  • How many Speaker: Two
  • Speaker Profiles:
    • Allen Downey, Ph.D., Professor, Computer Science, Olin College
    • Jeffrey Yau, Ph.D., Chief Data Scientist, AB
  • Registration cost: Free Entry 
  • Who were the major sponsors: Data Science BOSTON #ODSC

3. Deep Learning for Robotics Summit, 2018, Boston

  • Conference City: Boston, USA
  • About: The conference covered Business Applications and AI Trends.
  • Event Date: 23 - 24th May, 2018
  • Venue: 510 Atlantic Avenue, InterContinental, Boston
  • Days of Program: Two
  • Timings: 8:15 AM - 5:00 PM
  • Purpose: The purpose of the summit was to learn how to leverage new AI methods to solve problems in the organization and discover the latest advancements in deep learning.
  • How many Speakers: 60
  • Speaker Profiles:
    • Animesh Garg, Senior Research Scientist/Research Scientist, 
    • NVIDIA AI Research Lab/Stanford AI Lab 
    • Jane Hung, AI Resident, Uber AI Labs.
  • Registration cost: Standard Pass - $1495, Student / Academic Pass - $495, Startup Pass  - $595
  • Who were the major sponsors:

    • Intel, 
    • Forbes, 
    • Accenture, 
    • IBM, 
    • Graphcore.    

    4. Deep Learning in Healthcare Summit, Boston

    • Conference City: Boston, USA
    • About conference: The attendees of the summit discovered the techniques to revolutionize medicine, diagnostics, and healthcare
    • Event Date: 25 - 26 May, 2017
    • Venue: Renaissance Boston Waterfront Hotel, 606 Congress Street, Boston, Massachusetts, 02210
    • Days of Program: Two
    • Timings: 8:15 AM - 5:00 PM
    • Purpose: The summit showcased the progressive methods and opportunities for deep learning and their impact across medicine & healthcare. 
    • Speaker Profiles:
      • Junshui Ma, Senior Principal Scientist, Merck 
      • Tuka Alhanai, Ph.D. Candidate, MIT 
      • Muyinatu Bell, Assistant Professor, John Hopkins University. 
    • Registration cost: $1495
    • Who were the major sponsors: 
      • Medial,
      • EarlySign, 
      • Freebird,
      • Proscia. 

    Data Scientist Jobs - Boston

    Here is the logical sequence of steps you should follow to get a job as a Data Scientist.

    1. Getting started
    2. Mathematics
    3. Libraries
    4. Data visualization
    5. Data preprocessing
    6. Machine Learning and Deep Learning
    7. Natural Language processing
    8. Polishing skills

    If you are thinking to apply for a data science job, then follow the below steps to increase your chances of success:

    • Study: To prepare for an interview, cover all important topics, including-
      • Probability
      • Statistics
      • Statistical models
      • Machine Learning
      • Understanding neural networks
    • Meetups and conferences: Tech meetups and data science conferences are the best ways to start building your network or expanding your professional connections.
    • Competitions: Implement, test and keep polishing your skills by participating in online competitions like Kaggle. 
    • Referral: According to a recent survey, referrals are the primary source of interviews in data science companies. So, make sure your LinkedIn profile is up to date.
    • Interview: If you think you are all equipped for the interviews, then go for it.Learn from the questions that you were not able to answer and study them well for the next interview.

    A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs. 

    In this modern business scenario that is generating tons of data every day, the role of a Data Scientist is becoming all the more important. This is because the data generated is a gold mine of patterns and ideas that could prove to be very helpful in the advancement of a business. It is up to the data scientist to extract the relevant information and make sense of it in order to benefit the business.

    Data Scientist Roles & Responsibilities:

    • Fetch data that is relevant to the business from among the huge amount of data that is available in the form of Structured as well as Unstructured Data.
    • Organize and analyze the data that is extracted from the piles of data.
    • Create Machine Learning techniques, programs, and tools in order to make sense of the data.
    • Perform statistical analysis for relevant data and predict future outcomes from it.

    The national average salary for a Data Scientist is $125,310/yr in Boston, MA. The salary for a data analyst is $73,800/yr while that of a database administrator is $114,714/yr in Boston, MA.

    The career path in the field of Data Science can be explained in the following ways:

    Business Intelligence Analyst: A business intelligence analyst develops and provides new business intelligence solutions. They may be tasked with defining, reporting on or otherwise developing new structures for business intelligence in ways that will serve a specific purpose. Report writing can be a significant component of this role. They ensure that the business is always in the best position to utilize its most valuable information in a manner that is conducive to its success.

    Data Mining Engineer: A Data Mining Engineer performs the following responsibilities:

    • Generating derived datasets
    • Detecting and remediating production issues
    • Tracking data usage and data access performance
    • Creating data flow and transformation pipelines
    • Automating data reliability and quality checking.
    • Providing data access via databases and API services

    Data Architect: The responsibilities of Data Architect is to create database solutions, evaluating requirements, and preparing design reports.

    • Data warehousing
    • Elastic working and functioning.
    • Data cleaning
    • Data modeling
    • ETL working

    Data Scientist: The chief data officer is a senior executive responsible for the utilization and governance of data across the organization through data management, ensuring data quality and creating data strategy. The various roles include the following:

    • Advanced predictive algorithm
    • NoSQL
    • Data advanced algorithm
    • ETL Logic   
    • Big Data processing       

    Senior Data Scientist: The Senior Data Scientist oversees the activities of the junior data scientists and supervises and provides advanced expertise on statistical and mathematical concepts for the broader Data and Analytics department. 

    Below are the top professional organizations for data scientists in Boston, MA – 

    • Data Science for Online Learners
    • Vivli Datathon
    • Data Science Professional Development Boston
    • Boston Women in Data
    • Big Data Developers in Boston

    Referrals are the most effective way to get hired in Boston, MA. Some of the other ways to network with data scientists are:

    • Data science conference and workshops
    • An online platform like LinkedIn
    • Paper presentations
    • Data Science competitions
    • Follow influential data scientists
    • Meetups

    There are several career options for a data scientist in Boston, MA–

    1. Data Scientist
    2. IoT Specialist
    3. Statistician
    4. Data Administrator
    5. Data Analyst
    6. Decision Scientist
    7. Business Analyst
    8. Data/Analytics Manager

    We have compiled the key points, which the employers generally look for while hiring data scientists:

    • Education: A Bachelor’s degree is a must in Computer Science or related fields. Many institutes are also introducing special courses on data science. Having a Master’s or a Ph.D. in Data Science will boost up your profile and will get you closer to your dream job.
    • Programming: Python is a great programming language for data scientists. It is the preferred choice for data scientists. Apart from that SQL is a must no matter what you specialize in.
    • Machine Learning: Machine Learning is a fast emerging technology. It will be beneficial for you to gain some hands-on experiences on this. It is recommended to have good knowledge of various Supervised and Unsupervised learning algorithms such as:

      • Random Forest
      • Clustering (for example K-means)
      • Logistic Regression
      • K Nearest Neighbor
      • Linear Regression.
    • Projects: Many projects are available to take up online. You can refine your search by choosing your level of difficulty, starting from beginner to proficient, based on your knowledge and confidence with the tools and technology. Such projects will boost your profile and help you get closer to your desired job.

    DS with Python - Boston

    • Python is a multi-paradigm programming language - this means to say that the various facets of Python are most suited for the field of Data Science. It is structured and object-oriented programming language that contains several libraries and packages that are useful for the purposes of Data Science. 
    • The inherent simplicity and readability of Python as a programming language makes it a language that is preferred by data scientists. The huge number of dedicated analytical libraries and packages that are tailor-made for use in data science are some of the main reasons why data scientists prefer the use of Python for Data Science projects, as opposed to any other programming language.
    • Another great thing about Python which makes it the language of choice for data scientists is the broad and diverse range of resources that are available at the disposal of a data scientist, should he/she get stuck at a particular point or problem while developing a Python program or model for Data Science.
    • The vast Python community is another big advantage that Python has over other programming languages. Since there are millions of developers working on the same problems with the same programming language every day, it is very easy for a developer to get help in resolving his/her problems because the chances are that someone else had been stuck at the same problem in the past and its resolution has already been found. If no one else has encountered a similar problem, the Python community is quite helpful and tries its best to help their fellow Data Science in Python developers.

    As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.

    • R
    • Python
    • SQL
    • Java
    • Scala

    Follow these steps to successfully install Python 3 on windows:

    Download and setup: Go to the download page and set up your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use python’s functionalities from the terminal.

    Alternatively, you can also install python via Anaconda as well. Check if python is installed by running the following command, you will be shown the version installed:

    python --version

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

    python -m pip install -U pip

    Note: You can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.

    To install python 3 on Mac OS X, just follow the below steps:

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

      You should also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.

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      Attended Data Science with Python Certification workshop in January 2020

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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.

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      Knowledgehut is among the best training providers in the market with highly qualified and experienced trainers. The course covered all the topics with live examples. Overall the training session was a great experience.

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      Data Science with Python Certification Training in Boston

      About Boston 

      This dynamic city has been witnessing many significant events in US history as part of the American Revolution. This includes the Boston Tea Party, the Siege of Boston, the Boston Massacre, and the Battle of Bunker Hill. Today, it continues to be a critical port and manufacturing zone as well as a place for education and culture. The city has distinguished itself to have the country’s first public school, the Boston Latin School and the first subway system. Its several world-renowned colleges and universities make Boston a global center of higher education and medicine. The city is perceived to be a frontrunner in innovation for an array of reasons. Boston's financial profile includes banking and financial services, government activities, and other professional and business services.  

      Data Science with Python Training in Boston 

      If you want a lucrative career in Data Science, our comprehensive Data Science and Python training will help kick-start your journey. In addition to this, we have numerous in-demand certifications you can explore such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile and others.  

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