{"id":12971,"date":"2021-10-11T11:13:32","date_gmt":"2021-10-11T18:13:32","guid":{"rendered":"https:\/\/www.springboard.com\/?p=12971"},"modified":"2022-10-20T17:50:55","modified_gmt":"2022-10-21T00:50:55","slug":"open-source-data-science-tools","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/open-source-data-science-tools\/","title":{"rendered":"Top 15 Open-Source Data Science Tools to Learn (and Use) in 2024"},"content":{"rendered":"\n

Data science is on a continued upswing\u2014both in terms of career opportunities as well as in the ways that organizations, across industries, are making use of it. Everyone from HDFC bank and Flipkart to the government of India is leveraging data science platforms, methods, and techniques. Clearly, there\u2019s no time like the present to build a data science career. And a great way to start is by developing skills in a few data science tools.<\/p>\n\n\n\n

Our Springboard experts recommend the top 15 data science tools to learn this year. Don\u2019t rush to learn them all. Work on becoming conversant in as many as you can, but get hands-on experience in one or two by experimenting with them on your data science projects.<\/p>\n\n\n\n

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Top Open-Source Data Science Tools<\/h2>\n\n\n\n

Data Mining and Transformation<\/h3>\n\n\n\n

Strictly speaking, data mining is about identifying patterns in large datasets. But in practice, it has come to include extraction, collection, storage and analysis of information. There are tools that can do one or more of these tasks. Our top three are:<\/p>\n\n\n\n

  1. Weka<\/a> is a popular tool used for data mining<\/a>, pre-processing and classifying data. Weka\u2019s GUI simplifies classification, association, regression, and clustering, providing statistically robust results. <\/li>
  2. Scrapy<\/a> is best for writing web spiders that will crawl websites and extract data (you know, scraping). Written in Python, Scrapy is fast and powerful. CareerBuilder<\/a> uses Scrapy to collect data on job offers across multiple sites.<\/li>
  3. Pandas<\/a> is a popular data wrangling software, also written in Python. It is perfect for working with numerical tables and time-series data. It provides flexible data structures that make data manipulation easy. It is the backbone of recommendation engines from Netflix and Spotify. <\/li><\/ol>\n\n\n\n

    Data Analysis and Big Data Tools<\/h3>\n\n\n\n

    Once the data has been collected and processed, it\u2019s time for analysis. Here, you need a tool to get the data ready for model training and refining predictions. Some of the best ones are: <\/p>\n\n\n\n

    1. KNIME<\/a>, or Konstanz Information Miner, in full, provides end-to-end data analysis, and integration and reporting. Its GUI allows users to perform pre-processing, analysis, model building and visualization with minimal programming.<\/li>
    2. Hadoop<\/a> is a software framework primarily used for storage and processing of big data on a distributed model. This allows the data to be processed faster, and any hardware failures to be handled better. <\/li>
    3. Spark<\/a> from Apache is an analytics engine for big data. With Spark, you can run large-scale workloads of peta-bytes of data and build applications faster, and deploy them comfortably across virtual machines, containers, on-prem or on the cloud. <\/li>
    4. Neo4J<\/a> is a graph database management platform, and the most popular one at that. Unlike a relational database, graph databases store connections along with the data, and Neo4J helps users detect hard-to-find patterns on such data.<\/li><\/ol>\n\n\n

      Get To Know Other\tData Science Students<\/h4>
      \"Isabel<\/a>

      Isabel Van Zijl<\/p>

      Lead Data Analyst at Kinship<\/p><\/div>

      <\/div>

      Read Story<\/a><\/p><\/div><\/div>

      \"Samuel<\/a>

      Samuel Okoye<\/p>

      IT Consultant at Kforce<\/p><\/div>

      Read Story<\/a><\/p><\/div><\/div>

      \"George<\/a>

      George Mendoza<\/p>

      Lead Solutions Manager at Hypergiant<\/p><\/div>

      Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

      Model Deployment<\/h3>\n\n\n\n

      One of the key purposes of data science<\/a> is in developing machine learning models on the data. These models can be logical, geometric or probabilistic models. Here are some tools you can use for model-building. <\/p>\n\n\n\n

      1. TensorFlow.js<\/a> is the JavaScript edition of the popular machine learning framework, TensorFlow. You can develop models in JavaScript or Node.js and use TensorFlow.js to deploy them over the web on the client browser.<\/li>
      2. MLFlow<\/a> is a machine learning lifecycle management platform \u2014 from building and packaging to deploying models. If you\u2019re experimenting with multiple tools or building several models, MLFlow helps manage all of it from one place. You can integrate library, language or algorithm with the product. <\/li><\/ol>\n\n\n\n

        Data Visualization<\/h3>\n\n\n\n

        Data visualization needs to be more than just a visual representation of data. Today, it needs to be scientific, visual and more importantly insightful. In that, it should go beyond reporting; it must present analytical reasoning through interactive visual interfaces. Here are some tools that can help visualize your data science projects.<\/p>\n\n\n\n

        1. Orange<\/a> is an easy to use data visualization tool with a large toolkit. In spite of being a GUI-based beginner-friendly tool, you mustn\u2019t mistake it for a light-weight one. It can do statistical distributions and box plots as well as decision trees, hierarchical clustering and linear projections. <\/li>
        2. With D3.js<\/a> or Data-Driven Documents (D3), you can visualize data on web browsers using HTML, SVG and CSS. It is popular with data scientists<\/a> for its capabilities in animation and interactive visuals. <\/li>
        3. Ggplot2<\/a> helps data scientists create aesthetically pleasing and elegant visualizations, using R. So next time you want to really wow your audience, you know which library to choose for creating your visuals!<\/li><\/ol>\n\n\n\n

          Development Environments<\/h3>\n\n\n\n

          Like most programming, writing and deploying data science code can also be done more efficiently with an integrated development environment. IDEs offer code insights, test your code, help you identify errors easily, and even allow you to run your code directly with plugins. Here are some IDEs especially for data science-related code. <\/p>\n\n\n\n

          1. Jupyter Notebooks<\/a> is a web application that can host code, data, notes, equations, etc. \u2014 in other words, an interactive online document. If you\u2019re working on a project with other data scientists, Jupyter Notebooks is the perfect collaboration tool!<\/li>
          2. Zeppelin Notebooks<\/a> is a web-based environment where you can perform data analysis using many languages like Python, SQL, Scala etc. You can explore, share, analyze, and visualize data with Zeppelin Notebooks. <\/li>
          3. R Studio\u2019s<\/a> biggest attraction is that it integrates R-based tools into a single environment. You can write clean code, execute it, manage workflows and even debug it with R Studio. <\/li><\/ol>\n\n\n\n

            Companies are no longer just collecting data. They\u2019re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists \u2013 the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.<\/p>\n\n\n\n

            In this article, we\u2019ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you\u2019re more drawn out to the creative side or interested in the strategy planning part of data architecture, there\u2019s a niche for you. <\/p>\n\n\n\n

            Is Data Science A Good Career?<\/h2>\n\n\n\n

            Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It\u2019s an interdisciplinary field that keeps the work varied and interesting.<\/p>\n\n\n\n

            10 Data Science Careers To Consider<\/h2>\n\n\n\n

            Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.<\/p>\n\n\n\n

            <\/div><\/div><\/div><\/div>\n\n\n\n

            <\/p>\n\n\n\n

            Data Scientist<\/h3>\n\n\n\n

            Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research \u2013 all depending on the field they operate in.<\/p>\n\n\n\n

            They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. <\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            A data scientist\u2019s career starts with a solid mathematical foundation, whether it\u2019s interpreting the results of an A\/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. <\/p>\n\n\n\n

            Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses<\/a> and certifications that demonstrate their expertise and willingness to learn.<\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            The average salary of a data scientist in the US is $156,363<\/a> per year.<\/p>\n\n\n\n

            Data Analyst<\/h3>\n\n\n\n

            A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.<\/p>\n\n\n\n

            A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.<\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps<\/a> to acquire these skills. <\/p>\n\n\n\n

            For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.<\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            The average base salary of a data analyst is $76,892<\/a> per year.<\/p>\n\n\n\n

            Business Analyst<\/h3>\n\n\n\n

            Business analysts often have an essential role in an organization, driving change and improvement. That\u2019s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. <\/p>\n\n\n\n

            A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.<\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master\u2019s degree, especially in more complex industries or corporate environments.<\/p>\n\n\n\n

            Employers also value a business analyst\u2019s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.<\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            A business analyst can earn an average of $84,435<\/a> per year.<\/p>\n\n\n\n

            Database Administrator<\/h3>\n\n\n\n

            The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. <\/p>\n\n\n\n

            A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.<\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it\u2019s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. <\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            Database administrators earn an average salary of $77,391<\/a> annually.<\/p>\n\n\n\n

            Data Engineer<\/h3>\n\n\n\n

            Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).<\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. <\/p>\n\n\n\n

            Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.<\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            Data engineers earn a whooping average salary of $125,180<\/a> per year.<\/p>\n\n\n\n

            Database Architect<\/h3>\n\n\n\n

            A database architect\u2019s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. <\/p>\n\n\n\n

            Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.<\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            If you\u2019re aiming to excel as a database architect but don\u2019t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don\u2019t forget programming languages – SQL, Python, or Java. <\/p>\n\n\n\n

            If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard<\/a>.<\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            Data architecture is a very lucrative career. A database architect can earn an average of $165,383<\/a> per year.<\/p>\n\n\n\n

            Machine Learning Engineer<\/h3>\n\n\n\n

            A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. <\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.<\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K<\/a> per year, one of the highest-paying AI careers<\/a>.<\/p>\n\n\n\n

            Quantitative Analyst<\/h3>\n\n\n\n

            Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. <\/p>\n\n\n\n

            They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.<\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. <\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            A quantitative analyst earns an average of $173,307<\/a> per year.<\/p>\n\n\n\n

            Data Mining Specialist<\/h3>\n\n\n\n

            A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.<\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. <\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            Data mining specialists earn $109,023<\/a> per year.<\/p>\n\n\n\n

            Data Visualisation Engineer<\/h3>\n\n\n\n

            Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data\u2019s context. <\/p>\n\n\n\n

            General Requirements<\/h4>\n\n\n\n

            Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.<\/p>\n\n\n\n

            Average Salary<\/h4>\n\n\n\n

            The average annual pay of a data visualization engineer is $103,031<\/a>.<\/p>\n\n\n\n

            Resources To Find Data Science Jobs<\/h2>\n\n\n\n

            The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.<\/p>\n\n\n\n

            Job Boards<\/h3>\n\n\n\n

            When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. <\/p>\n\n\n\n

            Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.<\/p>\n\n\n\n

            Online Communities<\/h3>\n\n\n\n

            Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.<\/p>\n\n\n\n

            Network And LinkedIn<\/h3>\n\n\n\n

            Don\u2019t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the \u201cEasy Apply\u201d options, as you\u2019ll be competing with thousands of applicants who bring nothing unique to the table.<\/p>\n\n\n\n

            FAQs about Data Science Careers<\/h2>\n\n\n\n

            We answer your most frequently asked questions.<\/p>\n\n\n\n

            Do I Need A Degree For Data Science?<\/h3>\n\n\n\n

            A degree is not a set-in-stone requirement to become a data scientist<\/a>. It\u2019s true many data scientists hold a BA\u2019s or MA\u2019s degree, but these just provide foundational knowledge. It\u2019s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.<\/p>\n\n\n\n

            Does Data Science Need Coding?<\/h3>\n\n\n\n

            Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.<\/p>\n\n\n\n

            Is Data Science A Lot Of Math?<\/h3>\n\n\n\n

            It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.<\/p>\n\n\n\n

            What Skills Do You Need To Land an Entry-Level Data Science Position?<\/h3>\n\n\n\n

            To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you\u2019re acing problem-solving, critical thinking, and effective communication.<\/p>\n\n\n\n

            Since you\u2019re here\u2026<\/strong>Are you interested in this career track? Investigate with our free guide to what a data professional actually<\/em> does<\/a>. When you\u2019re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp<\/a> which will help you land a job or your tuition back!<\/p>\n","protected":false},"excerpt":{"rendered":"

            Data science is on a continued upswing\u2014both in terms of career opportunities as well as in the ways that organizations, across industries, are making use of it. Everyone from HDFC bank and Flipkart to the government of India is leveraging data science platforms, methods, and techniques. Clearly, there\u2019s no time like the present to build […]<\/p>\n","protected":false},"author":100,"featured_media":2492,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_eb_data_table":"","footnotes":""},"categories":[67],"tags":[],"marketing_tags":[],"class_list":{"0":"post-12971","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/12971"}],"collection":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/users\/100"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=12971"}],"version-history":[{"count":4,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/12971\/revisions"}],"predecessor-version":[{"id":35634,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/12971\/revisions\/35634"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/2492"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=12971"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=12971"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=12971"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=12971"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}