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Data Science for Beginners: 2023 - 2024 Complete Roadmap

Data Science Roadmap

What is Data Science?

Data Science is the art of intelligence that involves extracting meaningful information to gain insights. The process consists of gathering, storing, analyzing, and plotting data.

Who are Data Scientists? These are data experts who perform and apply statistics, machine learning, and analytical approaches to answer critical business questions. Data scientists utilize various techniques, such as visualization, to interpret and present their findings and results. They help forecast the future based on the patterns and findings that have been discovered.

Other Different Roles in Data.

Data Analysis - This is a method of querying, processing, providing reports, summarizing, and visualizing data to derive information to influence decision-making.
Data analysts understand cleaning, visualizing, and exploratory data analysis which helps companies or organizations make informed and better decisions.

Data Engineering - This is an intelligence that involves designing systems and building systems used for storing, analyzing, and collecting data.
To collect and organize data, data engineers are responsible for constructing and operating data pipelines. It is their responsibility to make sure that quality data is accessible and available.

Data Science Pillars:

  1. Statistics - This is a type of math that teaches how to collect and analyze data to answer critical questions to influence decisions.
  2. Domain knowledge - This is expertise in the business problem. This helps in collaboration and prowess in navigation in the field of research and in that industry.
  3. Computer Science - This entails knowledge of how computers work. As a result, it is also necessary to understand programming.
  4. Communicating and visualizing - The delivery of messages is essential in this process. Due to the importance of message delivery to the interpretation of data, it is important to consider it.
  5. Collaboration - DataScience relies on other departments for extraction, transformation, and loading of data. This requires effective teamwork in the field.

Tools of a Data Scientist.

  1. Programming tools - These are languages and tools used for programming - Python and its data frames (Numpy, Pandas, PyTorch, Scipy), R, Scala, Java., Jupyter, MongoDB, SQL, Julia, D3.js, Apache Spark.
  2. Machine Learning Tools - These are software tools used for Machine Learning. They are used according to various roles implemented - Scikit Learn, Accord.Net, Apache Mahout, TensorFlow, Weka, KNIME, Colab, Accors.Net, Shogun,, Rapid Miner, DataRobot, NLTK (Natural language toolkit).
  3. Visualization tools - These tools help data scientists present data by use of an easy human-understandable format. They rely on graphs, tables, dashboards, graphics, and many more. Seabon, Matplotlib, Gplot2, Lattice, Bokeh, Shinny, Power BI, Tableau, Infogram, Plotly, Matlab, MS Excel, Sisense, fusion charts, Qliqsense, DOMO, LookerVi, board, data wrapper (CSV)
  4. Cloud-Based Tools - These are tools available for easy access and real-time collection and usage of data. - BigML, Google Analytics, AWS, Terraform.

These are just to mention a few. You can look out for more according to the niche of your project. Some tools are more effective in various fields of use.

Important skills for a data scientist.

Technical Skills are:

  1. Statistics and Mathematics - Probability, Linear Algebra, Calculus.

  2. Machine Learning and Deep Learning - Able to train models, evaluate, and deploy them.

  3. Data Wrangling - The ability to convert raw data into usable and meaningful form.

  4. Programming - A data scientist can program in search of maximum querying. They Can learn Java, Python, R, and Scala. Choosing the most effective for the project.

  5. Visualization - A proficient data scientist knows how to present insights found.

  6. Data Management and Governance - Implement security, availability, usability, and integrity.

  7. Web Scraping - This involves extracting data from websites.

  8. Database management and querying - Querying and managing databases in use. SQL, MongoDB, Couch, file storage, Excel file storage,

  9. DSA - (Data Structures and Algorithms) - These help with maximum productivity while approaching a problem.

  10. Version control - Git, Git Lab, Bit bucket.

  11. Cloud computing - The access to resources from anywhere by authorized users.

  12. DevOps - The demand for real-time data is rising. The use of the CI/CD cycle is important to deliver real-time live results.

  13. Operating Systems - Linux, Windows, server OS, and other platforms of use.

  14. Data Extraction, Transformation, cleaning, and preparation for loading.

  15. Automation - using scripts to perform regular and repetitive tasks.

Soft skills are:

  1. Communication.
  2. Problem solving.
  3. Critical thinking.
  4. Decision making.
  5. Creative thinking.
  6. Business intelligence.
  7. Storytelling.
  8. Attention to detail.

Data Science Methodology

This is a lifecycle that involves the approach of a Data Science project.

  1. Business problem understanding - understand owners' needs and their internals. This identifies expectations.
  2. Data collection and storage. - Data acquisition plays a crucial role in helping understand what datasets are important.
  3. Data Preparation and Understanding - this involves understanding the dataset you are working on and the structure of the data (structured or unstructured). It also involves duplication, transforming, and handling missing values. Identifying the data variables is discovered here.
  4. Data Modeling and evaluation - Trends and insights are evaluated in this phase. The tools used in this phase include R, Python, Matlab, and SAS.
  5. Diagnostics and mining of data are executed here to produce a quality evaluation outcome. Prediction and description help us know the hits and misses of the models.
  6. Deployment - feedback is derived from this phase to test the capabilities of the models. Maintenance and monitoring help in recommending the way forward using reports, summaries, and experience.

Data science applications.

  1. Machine learning - teaching machines to interpret the right data for use.
  2. Internet searching - provides better results and is accurate for queries.
  3. Voice assistance - training in dialects and sounds.
  4. Health care - prioritization of surgery and effective treatment.
  5. Robotics and IoT - manufacturing and prediction of outcomes and responses.
  6. Marketing and E-commerce - increasing purchases and client conversion rates, recommending products, competitively advancing business.
  7. Education - providing insights into the performance of students' study behaviors.
  8. Weather prediction and calamity prediction like earthquakes and fires.
  9. Finance - data science provides insights into what is expected when it comes to the economy and expenditure. Helps analyze losses and income and expenditure maintenance.
  10. Technology - Data science has improved technology with very steep growth. Technology and big data are now working parallel to each other to provide a better experience.
  11. Travel - helps with recommendations for shorter routes.
  12. Crime - helps analyze crime rates, sources, and areas of crime for easy detection and prediction.

Benefits of data science.

A Data Scientist is an asset to the company.

A Data Scientist...

  1. Empowers the management to make better-informed decisions.
  2. Provides insights into KPIs (key performance indicators).
  3. Helps identify the underlying opportunities.
  4. Helps identify loopholes and areas of improvement in the business.
  5. Helps refine the target audience and maintain the audience.
  6. Enables a drive for better results.

Trends in Data Science.

  • Cognitive computing - Artificial intelligence in cybersecurity relies on ML algorithms.
  • Augmented reality - a great experience is enhanced due to the use of Big Data.
  • Automation - Machine learning is helping automate very crucial activities. Data collected is being used to accelerate automation.
  • Cloud data ecosystems- many companies are now migrating to cloud warehouses for faster clustering and access to data.


The world we are in now is already data-driven. It relies heavily on data to predict, describe, diagnose, and prescribe the best solutions to the problem at hand. The demand for data scientists will not glide downwards anytime soon.

The impact of data science is clear and the demand for knowledge is skyrocketing. Looking at the future, data will fuel everyday lives in how we eat, socialize, learn, and live. It is part of the existing environment.

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