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A Complete Guide on Data Science Roadmap

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03rd Apr, 2024
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    A Complete Guide on Data Science Roadmap

    Data science has become an undisputed topic of practice and discussion. The main reasons behind this scenario are the generation of gigantic volumes of data and the importance of data analysis in guiding decision-making. Data science uses different acquisition, preparation, storage, analytics, and interpretation techniques. The development of tools, methods, computational speeds, and automation has further confirmed the supremacy of data science. A huge opportunity exists to make a lucrative and rewarding career in this field to satisfy the necessary human resources. However, a career in this domain requires a specific cluster of data science skills to be developed through knowledge and practiced systematically. 

    This article explains a step-by-step approach of how to proceed on the right path towards making a successful career in data science. Thus, this article can be a road map for the aspiring generation interested in data science career-making, irrespective of their education or background. Beginners can start learning with the data science certification course from Knowledgehut. 

    Complete Data Science Roadmap

    Learn the Fundamentals

    Although data science is a specialized field, it is still accessible to fresh and experienced individuals alike. This is because data science is built on the core domains of math, statistics, and programming. So, people with good knowledge in either of these three domains can easily transition into data science. The roadmap for such individuals might be slightly different and of a shorter duration. For all the others, the road map begins with learning the basics in the three domains mentioned above. Learning the fundamentals of math and statistics will aid in a better understanding of data science concepts, while programming fundamentals will make it easier to work with various tools. It is also crucial to know the key terminology related to data, such as data formats, schemas, data mining, data exploration, data processing, etc. In the following sections, let us explore the critical steps in the road map. 

    Master One Data Science Field

    Natural Language Processing, Computer Vision, Machine Learning, Statistics, Mathematics, Programming, Data Analytics, and Business Intelligence are a few examples of Data science fields.

    Data science has many important applications in the above fields such as - 

    • Image Classification and Object Detection 
    • Fraud and Anomaly Detection 
    • Healthcare Management 
    • Language translation and Text Analytics 
    • Remote Sensing
    • and many more. 

    You need to choose your field of interest first, gather all relevant information about it and then decide on how to best proceed to make a career in the chosen field.

    There are three important roles commonly seen in all the above areas. These are data engineer, data analyst, and data scientist. The responsibilities associated with these roles are specific but interconnected. Hence, when you have to choose an option amongst these three roles, you need to clinically look at the skillsets of the profile required for each of them. 

    Data Engineer

    A data engineer is responsible for building and deploying data pipelines. A data engineer also handles various steps in the pipeline such as ETL (Extract-Transform-Load) functions of extracting, transforming, and loading the data into a warehouse. This data is received in different formats from various sources like RDBMS or NoSQL. Hence, proficiency in handling all types and sizes of data and distributed computing with knowledge of SQL, Oracle, MongoDB, or Cassandra as well as cloud computing is required. For big data at an enterprise level, a thorough understanding of Hadoop, Spark, Kafka, etc is necessary. One should also know programming languages like Java, Scala, and Python along with ETL tools like Talend, SAS, Apache Airflow. Familiarity with cloud services like Amazon AWS, Google Cloud Platform (GCP), Microsoft Azure, etc. is useful.

    Data Analyst

    The role of a data analyst starts once the data is available in the warehouse. He knows the company's business and must accumulate various aggregated information that is needed by the management. For this, a data analyst needs to extract the data from the data warehouse, process and explore it using exploratory data analysis (EDA). Thus, he must have good knowledge of Excel, SQL to retrieve specific information from stored data to list some important KPIs (key performance indicators). With these, he can prepare interesting and attractive dashboards that provide significant insights for management to make vital business decisions. Hence, knowledge of statistics and BI tools like Tableau and Power BI is essential.

    Data Scientist

    This is a position with a more exhaustive role and larger responsibilities than those of a data analyst. The tasks and responsibilities of a data scientists vary depending on the requirements of the company. 

    A data scientist is a key person in the organization who works on bringing valuable information from big data for data-driven decisions, thereby fulfilling business objectives. He decides on the types of data sources. He knows how to prepare the data, query it and perform EDA. He selects one or more potential models and algorithms to set up the machine learning/deep learning models based on the analysis results. Thus, a data scientist needs to have a strong knowledge of data science techniques, such as machine learning, deep learning, and statistical modeling. It is also essential to be familiar with model deployment and monitoring. 

    The data scientist is in the top position among the three and earns a higher salary. You can check out data science bootcamp salary to know more. This is obvious when one looks at the profile needed for a data scientist. So, looking at the skillsets, one has to choose as per his liking and strengths. 

    Master Data Skills

    To pursue any career in data science, it is important to master broad skills required for various expertise levels in the domain. These are discussed one by one in the following sections.

    Applied Statistics and Mathematics

    Mathematics 

    Performing data analysis to obtain insights from big data as well as training models using various machine learning algorithms require strong base of mathematics. Hence, data science careers require mathematical knowledge. Let's start by looking at the various branches of math used in data science to better understand what you truly need to know. 

    1. Linear Algebra

    Linear algebra is a field of mathematics involving linear equations, vectors, matrices, operations, sets, logarithms, exponential functions, eigenvalues, eigenvectors, etc. You will be applying linear algebra if you do a Principal Component Analysis (PCA) to reduce the dimensionality of your data. If you're using neural networks, linear algebra will also be used to represent and process the network. It's difficult to think of many models that don't need calculations based on linear algebra. 

    2. Probability

    Probability is a field of mathematics dealing with numerical representations of how likely an event will occur or not. Joint, conditional, and marginal probability are popular probability types used in decision trees and the Bayes theorem for machine learning. Probability distributions such as the Bernoulli distribution, uniform probability distribution, normal probability distribution, and exponential distribution are widely used for likelihood estimations, exploratory data analysis, pattern analysis, outlier detection, and so on. 

    3. Calculus

    Calculus is a field of mathematics concerned with the determination and characteristics of derivatives and integrals of functions using methods based on the summation of infinitesimal differences. The concept of gradient descent is an important aspect of machine learning and deep learning. It can be learned only with the knowledge of calculus. 

    Statistics

    Statistics provides an easy method to summarise, analyze, and visualize data in various formats. Knowing statistical methodologies and how to use them is beneficial in many phases of data science. Statistics are classified into different categories: 

    1. Descriptive Statistics

    By using descriptive statistics, we may gain a rudimentary knowledge of data. A few examples are finding the mean, median, mode, central tendency, range, standard deviation, variance, and correlation. This is the initial stage in analyzing quantitative data that can be easily visualized using graphs and charts. 

    2. Inferential Statistics

    We advance one step further with inferential statistics and get results from descriptive statistics data. Inferential statistics are more subjective and difficult to understand than descriptive statistics. Inferential statistics has two main objectives: 

    • Estimating parameters: Making estimates about populations 
    • Hypothesis testing: Comparing populations or assessing relationships between variables using samples. 

     Programming or Software Engineering

    The most important step in the data science journey is programming. The coding concepts and computing skills are required for every activity in data science. Some important programming concepts for data science, irrespective of the language, are as follows: 

    1. Data Structures

    It is essential to understand the concepts of arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, and schemas. Data structures can hold massive amounts of data and have a variety of functions like processing, maintaining, etc., to interact with data. 

    2. Control Structures

    Control structures are important for an application's workflow. Several control structures can help you determine the workflow, such as switch-case, while, do-while, and so on. 

    3. OOP Concepts

    OOP concepts serve as a foundation for learning any programming language. Most programming languages are object-oriented, making storing similar types of data simple. 

    To become familiar with the above topics, you can select any of the following programming languages, begin working on them, and continue the data science roadmap. 

    4. Python

    Python is a widely used open-source programming language. Python is extensively used in scientific and research groups because it is simple and has simple syntax. It is also more suited for rapid prototyping. Python has a huge set of libraries. The most important Python libraries for data science are NumPy, Pandas, Matplotlib, and Scikit-learn. 

    • NumPy: The NumPy library simplifies various mathematical and statistical operations. It also serves as the foundation for many aspects of the Pandas library. 
    • Pandas: The Pandas package is designed specifically to make dealing with data easier. It is developed on top of NumPy, which supports multidimensional arrays. 
    • Matplotlib: Matplotlib is a visualization library that allows you to quickly and easily create charts from your data. 
    • Scikit-learn: Scikit-learn is a well-known and powerful machine learning package that includes a large number of algorithms as well as tools for ML visualizations, pre-processing, model fitting, selection, and evaluation. It includes a variety of efficient algorithms for classification, regression, and clustering. Support vector machines, gradient boosting, k-means, and other algorithms fall under this category. 

    5. R

    R is another powerful language, just like Python. It is a commonly used open-source programming language for data science. For classification, clustering, statistical testing, and linear and nonlinear modeling, R includes a wide range of statistical and graphical tools. The top R libraries are as follows: 

    • dplyr: The dplyr package is used for data wrangling and data analysis. This package is used to perform various tasks with the R’s dataframe. The five functions Select, Filter, Arrange, Mutate, and Summarize form the foundation of the dplyr package. 
    • Tidyr: The tidyr package is used for cleaning or tidying up data. 
    • ggplot2: R is well known for its ggplot2 visualization package. It offers an interesting collection of interactive graphics. 

    In general, Python is easier to understand and more readable. So, if you are a beginner, you can start the data science journey with the Python programming language. However, if you are from a coding background, you can opt for R, too, as it has better libraries and tools for data analysis and visualization. 

    SQL

    Learning SQL is essential for data science. At the database level, SQL makes it simple to write queries and perform data grouping, selecting subsets of data, filtering, joining, merging, sorting, and other operations. Additionally, SQL is used by modern big data technologies like Hadoop and Spark to manage relational database architectures and analyze structured data. In SQL, you should be familiar with the following topics (but not limited to): 

    • Group By Clause: The SELECT statement and the SQL GROUP BY clause are used together to group similar pieces of data. Along with the group by clause, the Having Clause applies conditions. 
    • Aggregation Functions: An aggregate function adds together multiple values to produce a single value after performing a computation on them. For example, count, average, minimum, maximum, etc. 
    • Joins: This is a key topic that combines several tables to provide the necessary results. Ensure you understand the different join types, main, foreign, composite, etc. 

    Integrated development environment (IDE)

    An integrated development environment (IDE) is a software tool offering computer programmers extensive software development features. An integrated development environment (IDE) often contains a source code editor, build automation tools, and a debugger. 

    1. JupyterLab

    JupyterLab is an open-source web application that provides a user interface based on Jupyter Notebook. It enables users to collaborate with documents on Jupyter Notebook, developed out of IPython in 2014. Users can create and arrange workflows in data science, scientific computing, computational journalism, and machine learning using its versatile interface. 

    2. Spyder

    The Scientific Python Development Environment (Spyder) is a cross-platform, open-source IDE for data science. Spyder is an excellent choice for data scientists because of its powerful editing, code analysis tools, IPython Console, variable explorer, graphs, debugger, and help icon. 

    3. Atom

    Atom is a powerful ML & DS IDE that supports several languages other than Python, including C, C++, HTML, JavaScript, and others. Cross-platform editing, a built-in package manager, smart autocompletion, a file system browser, and multiple panes are among the features of the IDE. Furthermore, its plugins, languages, libraries, and tools are regularly updated, resulting in a highly customizable and exceptional Atom interface and experience. 

    4. PyCharm

    PyCharm is a Python IDE for data science and web development with intelligent code completion, on-the-fly error checking, quick fixes, etc. It also features a robust navigation system. Additionally, it contains an integrated library that includes programs like NumPy and Matplotlib. 

    5. Visual Studio Code 

    One of the most popular Python IDEs is Visual Studio Code. The IDE is well-known for its capabilities, such as IntelliSense, which goes beyond syntax highlighting and gives smart completions based on variable types, imported modules, and function definitions. VS Code is available in both free and premium editions. 

    Data Engineering

    Today, the volume and pace of data have established a distinct difference in the roles of a Data Scientist and Data Engineer, but with considerable overlap. With Data Engineering, it is possible to develop and create pipelines that can collect data from several sources and consolidate it into a single warehouse that represents the data consistently as a single source of truth. These data pipelines can transmit and modify data into a highly usable format when it reaches Data Scientists or other end users. Although it might sound simple, this part of data science requires high data literacy and programming skills. This is why it is extremely difficult to do effective data science without Data Engineering.

    So, beginners can start with learning SQL language and then move on to one RDBMS such as MySQL, Oracle, and one NoSQL database like MongoDB or Cassandra, as well as taking elementary courses in cloud technologies and frameworks like agile and scrum. 

    Data Collection and Wrangling (Cleaning)

    Data science relies on the availability of big data, data generated by IT devices at an overwhelming pace and in huge volumes. It is essential that the accumulated data be captured continuously at several different locations and then successfully transmitted to intended data storage locations like warehouses, lakes, or marts. Several data collection tools and techniques are available in the market, and companies tend to avail these based on their requirements. Now, the collected data is raw data that has no value. Messy data cannot tell us anything fresh or relevant. Big data can add significant value to organizations only when it is well-structured (ready for data analysis), cleansed (unwanted parts are removed), and verified data. This process can be called data wrangling. It is essential to filter out the relevant information as there might be noise (unwanted or irrelevant data points) present in the raw data. Additionally, data sorting can be included to prepare the data for further analysis. 

    As a beginner, you can learn the basics of web scraping and the techniques employed in data wrangling. 

    Exploratory Data Analysis

    The collected data cannot tell a story unless it is thoroughly analyzed. With Exploratory Data Analysis (EDA), we uncover the hidden trends and patterns in the pre-processed data set to summarize the main characteristics of the dataset. This helps businesses in making more data-driven decisions. Several statistical methods can be used to assess the dataset and its features. Further, an EDA is incomplete without visualizations, i.e., plots or charts. One important thing to note here is that this is a time-consuming project step and must be carried out carefully. The results of the EDA will enable the data scientist to build appropriate models for a specific project. 

    Beginners can start with a simple dataset from the toy datasets in popular libraries like Scikit learn, Seaborn, or Altair to carry out their first EDA. Later, more complex datasets like financial, retail, or healthcare datasets can be tried to check the acquired EDA skills. 

    1. Visualization

    The analyzed data in EDA needs to be graphically represented for better understanding. Apart from tables, typical charts like bar charts, line charts, scatter plots, histograms, and pie charts are used for presenting the data for Senior Management to make decisions. Sometimes in businesses, advanced charts like sunburst charts, tree maps, waterfall charts, and candlestick charts are also used to show the data in an impactful way. The chart selection depends on Data Analyst and the organizational requirements.

    Newbies can start building simple charts mentioned above in libraries like Matplotlib, Seaborn for Python, or ggplot in R for a few built-in datasets. 

    Machine Learning and AI

    Machine learning is a subset of AI (artificial intelligence). AI tries to develop thinking and performing abilities in machines similar to humans. Machine learning covers building different models with the help of well-known algorithms based on supervised or unsupervised learning. This is, therefore, a very important part of data science, which calls for skills in data engineering, programming, maths, and statistics. One must, therefore, learn about various algorithms like linear and Logistic Regression, Support Vector Machine, Random Forest, kNN, XGBoost, etc. For this task, Python or R can be used. 

    Deep Learning

    Machine learning has some limitations, especially when dealing with images or sequential data processing. The most popular and widely used technique is deep learning, where models are trained to greater depths to achieve higher accuracy in the above cases. Deep learning utilizes the same principle as what our brain uses. It adopts the neural network wherein input data is in the form of neurons, passing through different layers like convolution layers, hidden layers, pooling layers, fully connected layers, etc. Different activation functions like Softmax and Relu are applied to get the final output. Thus, different neural networks in deep learning are used in computer vision like image recognition, object detection, NLP, etc. This being a very important job role of a data scientist, one must fully learn the different neural networks required for deep learning. 

    1. Artificial Neural Network (ANN)

    ANN is one of the three types of networks commonly used. It is also known as Feed Forward Neural Network. ANN are used for a range of applications including image recognition, speech recognition, machine translation, and medical diagnosis, etc. 

    2. Convolutional Neural Network (CNN)

    A CNN is a Deep Learning system that takes an input picture, assigns random learnable weights plus biases to different features in the image, and achieves a clear difference between them. Thus, CNN's role is to compress the pictures into a format that is easily manageable while simultaneously retaining key components. This helps to give good and high-accuracy output for all computer vision problems. 

    3. Recurrent Neural Network (RNN)

    RNN is a type of ANN designed to deal with sequential data like sentences or text, video, etc. It has a series of layers and stacked up time step layers. It is a sort of mesh of both vertical and horizontal layers. The main difference between CNN and RNN is that RNN has time steps. Hence, it can process live streams like live videos. 

    Leverage Your Skills on Complex Projects

    Once you have obtained all the previously mentioned skills, it is time to implement them. This means we need to put the models on a cloud server, i.e., deployment of the machine learning or deep learning models as a web app that can be used in a browser. This is possible using several tools like Heroku, Netlify, Streamlit, Flask, and more. As a beginner, try some low-code tool like Streamlit or Gradio to deploy your first web app. 

    Additionally, beginners can try working all the steps on larger datasets with > 1 million rows and more than 30 features (columns). This will be a good practice to handle big data for pre-processing and help build confidence in the skills learned so far. 

    Further, projects such as fraud detection, x-ray classification, and object detection are quite challenging to try. These can prepare you for real-world data science projects on a smaller scale. 

    Track Your Learning Progress

    While learning each of the above units, you must make yourself aware of the progress made. For this, after every subtopic completion, you should attempt the exercises or assignments related to that subject, e.g., calculus, coding, data cleaning, data exploration, data queries, and similar ones. If you find that you are easily answering them accurately, it's a clear sign that you are marching correctly. You can also attempt machine learning and deep learning end-to-end projects with available datasets to build confidence before going for big data and complex projects. In every unit, whatever time you have allotted, 15 to 20% of that time must be kept for assignments every week. Another important tip is to revise the concepts intermittently for already covered topics. e.g., although you have completed calculus, matrices, or probability, you may need them very often in machine learning or deep learning; hence, it is better to be in touch with them frequently. You can also get some certifications in the above units of data science career path you chose. 

    Get Your Dream Job

    Once you are through with learning and grasping important aspects of all the units of the chosen career path, you can start applying through available job portals like Naukri.com, Monster.com, and LinkedIn. A well-drafted resume can help highlight your skill sets, certifications, and, most importantly, the end-to-end projects you have done. Don’t forget to mention your communication and soft skills in the CV. Keep patience and attempt with full confidence when an opportunity comes your way; you will succeed. 

    Upskill

    There is always scope for improvement in any job or career. That applies to any person working in the data science domain too. Therefore, one needs to upskill oneself and keep abreast with ever-changing technology. You can achieve it by - 

    • Earning professional certificates by joining advanced courses run by professional institutes
    • Participating in open competitions like Blogathons 
    • Writing technical blogs and evaluating responses 
    • Participating in discussion forums 
    • Attending seminars and webinars 
    • Reading the latest technical papers on topics of your interest in data science 
    • Subscribe to the popular and most sought-after journals and publications. 

    Get a deeper understanding of data science bootcamp salary from KnowledgeHut

    Conclusion

    This article highlights various important units of knowledge and skill sets that are necessary to pursue a data science career. In fact, these are to be acquired step by step, and all of these require specific time to be devoted to building adequate confidence. However, fortunately, resources in various forms, like books, online training, and YouTube videos, are available. With personal dedication and hard work, it is possible for any individual, either from a technical background or otherwise, to launch oneself into a data science career. 

    Frequently Asked Questions (FAQs)

    1What is the career path of a data scientist?

    A Junior Data Scientist can progress to higher levels with experience - 

    • Senior Data Scientist 
    • Lead Data Scientist 
    • Principal Data Scientist 
    • Data Science Manager 
    • Chief Data Scientist 
    • Director
    • Vice President (Data or Product Roles)
    2In what order should I learn data science?

    Beginners can start with learning the fundamentals of data science, followed by - 

    • programming languages like Python or R 
    • maths and statistics 
    • data preparation 
    • data analysis 
    • machine learning & deep learning 
    • model deployment 
    3Can I learn data science on my own?

    Learning data science by self-study using the road map presented in this article is possible.

    Profile

    Devashree Madhugiri

    Author

    Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms.
    She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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