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Wednesday, August 30 2023

What is Data Science – Lifecycle, Applications, Prerequisites, and Tools

Discover the intricacies of data science: from its lifecycle, tools, and roles to its impact on businesses. Delve into career opportunities, educational prerequisites, use cases, benefits, and real-world applications.

1. Introduction to Data Science

Definition and Overview

Data Science, often hailed as the “sexiest job of the 21st century” by the Harvard Business Review[1], is an interdisciplinary field that leverages techniques from statistics, mathematics, and computer science to extract insights and knowledge from structured and unstructured data. It’s not just about analyzing vast amounts of often unstructured data; it’s about making that data tell a story and inform decision-making.

In the age of information, businesses, governments, and individuals produce and consume an unprecedented amount of data. From the choices we make online to the sensors in our smartphones, data is everywhere. Data Science is the art and science of turning this vast amount of raw data, into actionable insights.

The Data Science Venn Diagram by Drew Conway, showcasing the intersection of Hacking Skills, Math & Statistics Knowledge, and Substantive Expertise.
What is Data Science - Lifecycle, Applications, Prerequisites, and Tools 2

Figure 1: The Data Science Venn Diagram by Drew Conway[2], showcasing the intersection of Hacking Skills, Math & Statistics Knowledge, and Substantive Expertise.

The Basic Principle Behind Data Science Techniques

At the heart of data science lies the essential process of data summarization, which allows professionals to quickly grasp the fundamental characteristics of vast datasets. This process often involves several steps:

  1. Data Collection: Gathering raw data from various sources, which could be databases, sensors, or user inputs.
  2. Data Cleaning: Processing the data to remove any inconsistencies, errors, or redundancies.
  3. Data Exploration: Using statistical methods to understand the nature of the data and identify patterns
  4. Feature Engineering: Transforming data to improve the performance of machine learning models.
  5. Modeling: Applying algorithms to the data to make predictions or classifications.
  6. Evaluation: Assessing the performance of the model.
  7. Deployment: Implementing the model in a real-world environment.
  8. Iterative Refinement: Continuously improving the model based on new data and feedback.

These steps are cyclic in nature. As new training data becomes available, models are refined, leading to better insights and more informed decisions.

It’s essential to understand that Data Science is not just about the techniques but also about the mindset. A good data scientist is not only technically skilled but also curious, skeptical, and has a knack for storytelling.

2. Data Science vs. Other Fields

Data Science versus Data Scientist

Data Science, as a field, encompasses a broad range of techniques and methodologies used to analyze and interpret complex data. On the other hand, a Data Scientist is an individual who practices these techniques. Think of Data Science as the entire ocean, while a Data Scientist is a skilled diver exploring its depths. A Data Scientist combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract actionable insights from data[3].

Data Science versus Business Intelligence

Business Intelligence (BI) and Data Science often overlap but serve different purposes. BI primarily focuses on descriptive analytics, and data reporting which is about understanding past events. It uses historical data to identify trends, analyze the causes of events, and interpret past results. Data visualization tools and dashboards are common in BI to represent this information.

Data Science, in contrast, delves deeper. It not only looks at the past but also predicts future events (predictive analytics) and prescribes actions (prescriptive analytics). While BI answers the “what happened?” question, Data Science answers “why did it happen?” and “what could happen next?”[4].

What is the difference between Data Science and Data Analytics?

Data Analytics is a subset of Data Science. While both fields aim to derive insights from data, their scope and focus differ. Analytics often seeks to provide a solution to a specific question. It’s more about data processing and performing statistical analysis on existing data datasets. Data Science, on the other hand, emphasizes algorithms and machine learning models to make predictions and decisions without human intervention[5].

What is the difference between Data Science and Business Analytics?

Business Analytics is primarily concerned with analytics that provide insights for business growth and operations. It’s more specific to business-related problems like costs, profits, and market trends. Data Science has a broader scope, encompassing various data types and sources, not just business-related data. It can be applied to diverse fields like healthcare, social sciences, and technology[6].

What is the difference between Data Science and Machine Learning?

Machine Learning (ML) is a subset of Data Science. It’s a method of data analysis that automates analytical model building. While Data Science encompasses a range of data processing techniques, ML specifically focuses on designing algorithms that allow computers to learn from and act on data. In essence, all machine learning is Data Science, but not all Data Science involves machine learning[7].

3. The Lifecycle of Data Science

Who Oversees the Data Science Process?

The Data Science process is typically overseen by a team of professionals, including Data Scientists, Data Engineers, Business Analysts, and domain experts. The collaboration between data science team members is critical to ensure that the right data point is being collected and processed, the right questions are being addressed, and the results align with business objectives.

A Data Scientist often plays a pivotal role in this team, bridging the gap between the technical aspects of data mining and processing and the business needs. They are responsible for designing the experiments, building models, and interpreting the results. However, the success of a Data Science project is a collective effort, requiring clear communication and collaboration among all stakeholders[8].

Steps to Become a Data Scientist

Becoming a Data Scientist is a journey that involves a mix of formal education, practical experience, and continuous learning. Here are the general steps:

  1. Educational Background: A bachelor’s degree in a related field such as Computer Science, Statistics, or Engineering is often the starting point. Many Data Scientists also pursue master’s or Ph.D. degrees in Data Science or related disciplines. The theoretical skills gained from formal programs enable data professionals with the right math and mindset required for interpreting data.
  2. Skill Development: Acquire proficiency in programming languages like Python or R, and tools like SQL. Familiarize yourself with machine learning libraries and data visualization tools.
  3. Practical Experience: Engage in real-world projects, internships, or Kaggle competitions to apply theoretical knowledge.
  4. Specialization: As the field of Data Science is vast, many professionals choose to specialize in areas like Deep Learning, Natural Language Processing, or Time Series Analysis.
  5. Continuous Learning: The field is ever-evolving. Regularly update your skills through online courses, workshops, and seminars[9].

What Does a Data Scientist Do?

A Data Scientist wears many hats with the primary responsibility to analyze data, identifying data patterns, and derive meaningful insights that are actionable to improve business operations. This involves:

  • Data Collection and Cleaning: Gathering data from various sources and ensuring its quality and reliability.
  • Exploratory Data Analysis (EDA): Understanding the underlying patterns and structures in the data.
  • Feature Engineering: Transforming data to improve the performance of predictive models.
  • Machine learning Algorithms: Develop and apply algorithms to efficiently solve for business challenges
  • Model Building: Designing, training, and testing algorithms to predict outcomes or classify data.
  • Interpretation and Communication: Translating the results into actionable business insights and presenting them to stakeholders.
  • Iterative Improvement: Refining models based on feedback and new data[10].

What kinds of problems do data scientists solve?

Data Scientists tackle a myriad of problems across industries. Here are some examples of business challenges that a data scientist may work on:

  • E-commerce: Personalized product recommendations to enhance user experience.
  • Healthcare: Predicting disease outbreaks or patient readmissions.
  • Finance: Detecting fraudulent transactions in real time.
  • Transportation: Optimizing delivery routes for logistics companies.
  • Entertainment: Designing data science algorithms for content recommendation on streaming platforms.
  • Public Policy: Analyzing social data to inform policy decisions[11].

4. Prerequisites and Eligibility for Data Science

Data Science Prerequisites

Data science, often hailed as the “sexiest job of the 21st century”[1], is a multidisciplinary field that requires a diverse set of skills. Aspiring data scientists often wonder what prerequisites they need to venture into this dynamic domain. Here’s a breakdown:

  • Mathematical Foundation: A strong grasp of mathematics, especially in areas like linear algebra, calculus, and statistics, is crucial. These mathematical tools form the backbone of many algorithms and models used in data science.
  • Programming Skills: Proficiency in programming languages such as Python and R is essential. Python, in particular, has become the de facto language for data science due to its simplicity and the vast array of libraries like Pandas, NumPy, and Scikit-learn[3].
  • Domain Knowledge: While technical skills are vital, understanding the domain or industry you’re working in can be equally important. This knowledge allows data scientists to ask the right questions and tailor their analyses to the specific challenges of that domain.
  • Data Management: Handling large datasets requires knowledge of databases and SQL. Familiarity with data warehousing and big data tools like Hadoop and Spark can also be beneficial.
  • Machine Learning: A fundamental understanding of machine learning algorithms and techniques is crucial, especially for those aiming to specialize in this subfield of data science.
  • Data Visualization: The ability to present complex data findings in a visually compelling manner is essential. Tools like Matplotlib for Python or ggplot2 for R are commonly used for this purpose.
  • Soft Skills: Beyond the technical, soft skills like problem-solving, communication, and teamwork play a pivotal role. Data scientists often need to collaborate with other departments, explain their findings, and influence decision-making processes.

What is the data science course eligibility?

For those looking to formalize their data science education, many institutions and online platforms offer courses and degrees in data science. The eligibility criteria can vary, but typically include:

  • Educational Background: A bachelor’s degree in a related field such as computer science, mathematics, physics, or engineering is often required. Some advanced courses or degrees might demand a master’s degree or equivalent.
  • Work Experience: Some professional or postgraduate programs prefer candidates with work experience, especially in roles related to data analysis or programming.
  • Programming Test: Given the importance of programming in data science, some institutions might conduct a test to assess the applicant’s coding skills.
  • Statement of Purpose: Many courses ask for a statement of purpose where candidates explain their motivation to study data science and how it aligns with their career goals.
  • Recommendation Letters: Some programs might require recommendation letters, typically from academic or professional references.

It’s worth noting that while these are common eligibility criteria, the exact requirements can vary based on the institution or platform offering the course. As the field of data science continues to evolve, there’s an increasing emphasis on a diverse cohort of students, and many programs are becoming more inclusive in their admissions processes.

While there are certain prerequisites and eligibility criteria for formal data science education, the field is known for its diversity. Many successful data scientists come from varied backgrounds, bringing unique perspectives and skills to the table. The key is a passion for data, continuous learning, and the perseverance to solve complex problems.

5. Applications and Use Cases of Data Science

Applications of Data Science

Data science is used in almost every industry, revolutionizing processes, enhancing decision-making, and providing a competitive edge to businesses. Here are some of the prominent applications of data science across various sectors:

  • Healthcare: Data science aids in predicting disease outbreaks, personalizing patient treatment plans, and optimizing hospital resource allocation. For instance, Google’s DeepMind has developed an AI that can spot eye diseases in scans[14].
  • Finance: Financial institutions leverage data science for fraud detection, risk assessment, and algorithmic trading. JPMorgan’s COIN program uses machine learning to review legal documents and extract essential data points and clauses[15].
  • E-Commerce: Companies like Amazon and Netflix use data science to analyze customer preferences, optimize search results, and provide personalized recommendations, enhancing the user experience.
  • Transportation: Ride-sharing services like Uber and Lyft use data science for price optimization, route planning, and demand forecasting.
  • Energy: Data science helps in predicting equipment failures, optimizing power generation, and enhancing energy efficiency. GE’s Predix is a software platform that uses data science to monitor and optimize industrial equipment[16].

Data Science Use Cases

To further illustrate the impact of data science, let’s delve into some specific use cases:

  • Sentiment Analysis: Companies analyze social media data to gauge public sentiment about their products, helping them tailor marketing strategies and address customer concerns.
  • Predictive Maintenance: Industries can predict when machinery will fail, allowing them to perform maintenance just in time to avoid unplanned downtime.
  • Customer Segmentation: Businesses can group customers based on purchasing behavior, preferences, and other factors, enabling targeted marketing campaigns.
  • Fraud Detection: By analyzing transaction patterns, companies can identify and prevent fraudulent activities in real time.

What is Data Science Used For?

Data science is a versatile field with applications ranging from improving business operations to addressing societal challenges. It’s used for:

  • Decision Making: By analyzing past data, companies can make informed decisions, reducing risks and maximizing returns.
  • Innovation: Data science drives innovation, leading to the development of new products and services tailored to customer needs.
  • Operational Efficiency: Through process optimization and resource allocation, data science enhances efficiency and reduces costs.
  • Tackling Global Challenges: Data science plays a pivotal role in addressing global issues like climate change, healthcare, and food scarcity.

Predictive Analysis

Predictive analysis, a subset of data science, involves using past data to forecast future events. It’s widely used in areas like:

  • Sales Forecasting: Companies can predict future sales based on historical data, helping them manage inventory and resources.
  • Credit Scoring: Financial institutions assess the likelihood of a borrower defaulting on a loan.
  • Healthcare: Predictive models can forecast disease outbreaks or patient admissions, aiding in resource planning.
  • Stock Market: Traders use predictive analysis to forecast stock price movements, guiding their investment strategies.

The applications and use cases of data science are vast and continually evolving. As technology advances and more data becomes available, the potential of data science to drive innovation and improve our lives will only grow.

6. Benefits of Data Science

What are the Benefits of Data Science for Business?

Data science has emerged as a transformative force for businesses across various sectors. Its benefits are manifold, and here we explore some of the most significant advantages it offers to enterprises:

  • Informed Decision Making: Data science enables businesses to make data-driven decisions by providing actionable insights from vast amounts of data. This results in better strategies and outcomes[17].
  • Enhanced Customer Experience: By analyzing customer behavior and preferences, businesses can offer personalized experiences, leading to increased customer satisfaction and loyalty[18].
  • Operational Efficiency: Data science can streamline operations by identifying inefficiencies and suggesting optimal processes. This not only reduces costs but also improves the quality of service[19].
  • Risk Management: Through predictive analytics, businesses can foresee potential risks and devise strategies to mitigate them. For instance, financial institutions can predict loan defaults and take preventive measures[20].
  • Innovative Products and Services: Data science can identify market gaps and customer needs, leading to the development of new products and services that cater to specific demands.
  • Competitive Advantage: In today’s data-driven world, businesses that leverage data science have a distinct edge over their competitors. They can respond faster to market changes, predict trends, and offer superior customer experiences[21].

Data Science Career Outlook and Salary Opportunities

The demand for data science professionals has skyrocketed in recent years, making it one of the most sought-after careers. Here’s a glimpse into the career prospects and earning potential in this domain:

  • Growing Demand: According to the World Economic Forum, data scientists and analysts are among the top emerging roles in the job market[22].
  • Attractive Salaries: Data scientists command lucrative salaries. A report by Glassdoor ranked data scientist as one of the top-paying jobs in the US, with a median base salary of over $110,000[23].
  • Diverse Opportunities: Data science professionals are needed across various sectors, including healthcare, finance, retail, and technology. This offers a plethora of opportunities to work on diverse projects and challenges.
  • Continuous Learning: The field of data science is ever-evolving. This necessitates continuous learning, making it an exciting career for those who thrive on challenges and wish to stay at the forefront of technological advancements.

The benefits of data science are profound, both for businesses and professionals in the field. As technology continues to advance and the importance of data grows, the impact and relevance of data science will only amplify.

7. Tools and Techniques in Data Science

Data science is a multifaceted field that leverages a variety of tools and techniques to extract and analyze data, and derive insights from from it. These data science tools are often categorized based on their primary function or the specific tasks they are designed to handle. Here, we delve into some of the most prominent data science tools around, organized under thematic categories.

Data Analysis and Data Exploration

Pandas: An open-source data analysis and manipulation tool built on top of the Python programming language. It provides data structures for efficiently storing large datasets and tools for reshaping, aggregating, and filtering data[24].

Scikit-learn: A machine learning library for Python. It features various algorithms for classification, regression, clustering, and dimensionality reduction. It’s known for its simplicity and efficiency, making it a favorite among data scientists[25].

Natural Language Processing (NLP)

NLTK (Natural Language Toolkit): A leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources, along with a suite of text processing libraries for classification, tokenization, and parsing[26].

Huggingface: A transformative tool in the NLP landscape, Huggingface offers a vast collection of pre-trained models and datasets. It has democratized access to state-of-the-art NLP techniques, allowing developers to implement powerful models with just a few lines of code[27].

Interactive Computing and Development Environments

Jupyter Notebook: An open-source web application that allows for the creation and sharing of documents containing live code, equations, data visualizations, and narrative text. It’s a preferred tool for data cleaning, statistical modeling, and machine learning[28].

Noteable: A modern data science platform that integrates seamlessly with Jupyter notebooks. It offers a collaborative environment, making it easier for teams to work together on complex data science projects[29].

Deep Learning and Neural Networks

TensorFlow and Keras: TensorFlow is an end-to-end open-source platform for machine learning, while Keras is a high-level neural networks API written in Python. Both are widely used for deep learning applications, from image recognition to natural language processing[30].

Data Visualization

Matplotlib and Seaborn: These are Python libraries for creating static, animated, and interactive visualizations. While Matplotlib is versatile and can produce a wide array of plots, Seaborn is built on top of Matplotlib and provides a higher-level interface for creating aesthetically pleasing visualizations[31].

In addition to the tools mentioned above, there are numerous other tools and libraries available for specialized tasks within various data science projects. The choice of tool often depends on the specific requirements of the project and the preferences of the data scientist.

8. Finding Your Place in Data Science

Data science is a vast domain with a plethora of roles and specializations. Whether you’re a budding data enthusiast or a seasoned professional looking to pivot into this field, understanding where you fit can be a transformative journey. This section aims to guide you through the myriad of opportunities in data science and help you find your niche.

The Multifaceted World of Data Science

Data science isn’t just about crunching numbers or building algorithms. It’s an interdisciplinary field that requires a combination of technical skills, domain expertise, and soft skills. The roles in data science can be broadly categorized based on the primary responsibilities and the expertise required.

Data Analyst

Data analysts are the detectives of the data science world. They sift through datasets to identify trends, analyze results, and generate reports. Their primary tools include statistical methods and data visualization techniques. If you have a keen eye for detail and love finding patterns in data, this might be your calling[32].

Data Engineer

Data engineers lay the foundation for data analysis and machine learning by ensuring efficient data extraction, data storage, data quality, data models, accessibility and data discovery. They design, construct, and maintain large-scale data processing systems that combine multiple data operations (data extraction, cleansing etc), often referred to as data pipelines, and the infrastructure to do all that. Their work ensures that all other data professionals are able to effectively leverage business data. They work closely with data architects and are proficient in SQL, Python, and other technologies[33].

Machine Learning Engineer

Machine learning engineers design, implement, and deploy models in live environments like an application or a website. They have a deep understanding of algorithms and computational statistics. They are often experienced software engineers who have a keen interest in building complex quantitative algorithms using statistical and mathematical skills and deploying them in live, customer facing environments to achieve business objectives. If you’re fascinated by AI and want to develop ML models, this role might appeal to you[34].

Data Scientists

Data scientists are the bridge between the technical and non-technical sides of an organization. They use a combination of programming, statistical skills, and domain knowledge to extract meaningful insights and build data-driven solutions. They often work on complex problems that require creative solutions[35].

Domain-specific Roles

Apart from the general roles, there are domain-specific roles like bioinformatics scientists who work on biological data or quantitative researchers in the finance sector. These roles require domain expertise combined with quantitative data science skills[36].

Finding Your Fit

To find your place in data science:

  1. Assess Your Skills and Interests: Reflect on your strengths, skills, and areas of interest. Do you enjoy coding, or are you more inclined towards statistical analysis?
  2. Gain Domain Knowledge: If you’re passionate about a particular domain, be it healthcare, finance, or e-commerce, delve deeper into it. Domain expertise can be a significant advantage in data science roles.
  3. Network: Engage with professionals in the field, attend workshops, webinars, and conferences. Networking can provide insights into various roles and the skills required.
  4. Continuous Learning: The field of data science is ever-evolving. Stay updated with the latest trends, tools, and techniques.

Remember, finding your place in data science is a journey, not a destination. As the field evolves, there will be new roles and opportunities. Stay curious, keep learning, and you’ll find where you truly belong.

9. Related Solutions and Further Reading

The world of data science is vast and ever-evolving. While this guide provides a comprehensive overview different data science tools, there are numerous other solutions, methodologies, and resources available for those looking to delve deeper into specific areas of data science. Here are some related solutions and recommended readings to enhance your understanding and skills:

Advanced Data Science Platforms

  • Databricks: An integrated platform for data engineering, data science, machine learning, and analytics[37].
  • Alteryx: A platform that offers data blending, advanced analytics, and data visualization[38].
  • RapidMiner: A software platform providing data science solutions from data prep to model deployment[39].

Books for Further Reading

  • “Python for Data Analysis” by Wes McKinney: A guide to the nuts and bolts of manipulating, processing, cleaning, and crunching data with Python[40].
  • “The Data Science Handbook” by Field Cady and Carl Shan: A compilation of in-depth interviews with data scientists, providing a human-centric look at the field[41].
  • “Data Science for Business” by Foster Provost and Tom Fawcett: An introduction to the concepts and principles of data science and its application in business[42].

Online Courses and Tutorials

  • Coursera’s Data Science Specialization: A series of courses designed to launch your career in data science[43].
  • edX’s Data Science MicroMasters: A program that covers the core concepts of data science[44].
  • DataCamp: Offers hands-on learning and their courses are structured around specific data science topics[45].

10. Conclusion

Data science is undeniably one of the most impactful fields of the 21st century, influencing every industry and reshaping the way we think about data. Whether you’re just starting your journey or looking to advance your career, the opportunities in data science are limitless.

The key to success in this domain lies in continuous learning, practical application, and staying updated with the latest trends and technologies. With the right mindset and resources, you can harness the power of data to drive innovation, solve complex problems, and create value in any industry.

Remember, the career progression in data science is as challenging as any other field[46] but the journey of a thousand miles begins with a single step. Take that step today, and embark on a rewarding journey into the world of data science.

Advance Your Career with an Online Short Course

For those looking to further their expertise and enhance their credentials, numerous online platforms offer short courses in various data science techniques. These courses, often facilitated by industry experts, provide hands-on experience and insights into real-world applications of different data science tools[47].

“Data Dcience” and Other Common Misconceptions

Ever heard of “Data Dcience”?: It’s the quirky cousin of Data Science that loves to crash the party! Just kidding – it’s a common typo. Always double-check your spelling, or you might end up at the wrong party. πŸ˜‰

Data Science is Just About Crunching Numbers: Many believe that data science is just about number crunching, but it’s more about deriving meaningful insights and making informed decisions based on data.

All Data is Good Data: The assumption that more data always leads to better insights can be misleading. The quality and relevance of data are crucial.

Data Science and Statistics are the Same: While statistics is an essential tool in a data scientist’s toolkit, data science encompasses a broader set of skills, including programming, data wrangling, and domain expertise.

Machine Learning Will Replace Data Scientists: While machine learning automates many tasks, the expertise to interpret data and results, understand business contexts, and implement solutions is irreplaceable.

Data Science is Only for Tech Companies: Data science applications span across industries, from healthcare to finance to agriculture.

Every Company Needs a Data Scientist: While skilled data scientists can benefit every company, not every company is ready or has the infrastructure to support a data science team.

Data Scientists Work Alone: Contrary to the lone genius myth, the data science process is often collaborative, involving teams with diverse skills.

Data Science is Just a Buzzword for Data Analysis: Data science involves a more comprehensive process, from data collection to deployment of data-driven solutions, whereas data mining and analysis is just one part of it.


  1. Data Scientist: The Sexiest Job of the 21st Century – Harvard Business Review
  2. Drew Conway’s Data Science Venn Diagram – Drew Conway
  3. What is Data Science? – IBM
  4. Business Intelligence vs. Data Science – What’s the Difference? – Jigsaw Academy
  5. Data Science vs. Data Analytics – Simplilearn
  6. Data Science vs. Business Analytics – Northeastern University
  7. Data Science vs. Machine Learning – Berkeley University
  8. Who is a Data Scientist? How to become a Data Scientist? – Edureka
  9. How to Become a Data Scientist – Dataquest
  10. Data Science – A Complete Guide – Built In
  11. Applications of Data Science Across Different Industries – Rice University
  12. Python Libraries for Data Science – Real Python
  13. Eligibility Criteria for Data Science Courses – ComputerScience.org
  14. DeepMind’s AI can spot eye disease in scans – BBC News
  15. JPMorgan’s Massive Guide to Machine Learning Jobs – eFinancialCareers
  16. GE’s Predix and the world of industrial IoT platforms – Network World
  17. The Advantages of Data-Driven Decision-Making – Harvard Business School
  18. How Big Data Is Changing The Way Businesses Understand Their Customers – Forbes
  19. Operational Efficiency: A Big Benefit of Big Data – InformationWeek
  20. Predictive Analytics in Operational Risk Management – Deloitte
  21. Competing in a Data-Driven World – McKinsey & Company
  22. The Future of Jobs Report 2020 – World Economic Forum
  23. 50 Best Jobs in America for 2019 – Glassdoor
  24. 10 minutes to pandas – Pandas Documentation
  25. Scikit-learn: Machine Learning in Python – Scikit-learn Official Website
  26. Natural Language Toolkit – NLTK
  27. Huggingface: The AI community building the future – Huggingface
  28. The Jupyter Notebook – Jupyter Project
  29. Noteable: A Modern Data Science Platform – Noteable
  30. TensorFlow: An end-to-end open-source machine learning platform – TensorFlow
  31. Matplotlib: Visualization with Python – Matplotlib Official Website
  32. What Do Data Analysts Do? – Investopedia
  33. Role of a Data Engineer – Dataquest
  34. Machine Learning Engineer vs. Data Scientist – Springboard
  35. What is a Data Scientist? – SaS
  36. The Core Competencies in Bioinformatics – PLOS Computational Biology
  37. Unified Analytics Platform – Databricks
  38. Alteryx: Data Science and Analytics – Alteryx
  39. RapidMiner: Data Science Platform – RapidMiner
  40. Python for Data Analysis – O’Reilly Media
  41. The Data Science Handbook – Wiley
  42. Data Science for Business – O’Reilly Media
  43. Data Science Specialization – Coursera
  44. Data Science MicroMasters – edX
  45. DataCamp: Learn Data Science Online – DataCamp
  46. The Data Science Career Path and Skills Progression – InterviewQuery
  47. “Online Data Science Courses” – Udemy

Posted by

Vivek Sinha