With the evolution of big data, Data Science is an emerging field that has gained a lot of popularity over the years. In 2023 and beyond, the demand for data scientists remains high, making it an attractive field for beginners to explore. This article will focus on the road map to becoming a Data Scientist for beginners and will provide some helpful tips to guide you on how to start off. Let’s dive in.
What is Data Science?
In simple terms, it can be defined as the process of collecting data, cleaning it, analyzing it and extracting information from it that can be useful for decision making.
A more technical definition from Michael Brodie(a lecturer at Havard university) states that:
Data Science is a body of principles and techniques for applying data analytic methods to data at scale, including volume, velocity, and variety, to accelerate the investigation of phenomena represented by the data, by acquiring data, preparing and integrating it, possibly integrated with existing data, to discover correlations in the data, with measures of likelihood and within error bounds. Results are interpreted with respect to some predefined (theoretical, deductive, top-down) or emergent (fact-based, inductive, bottom-up) specification of the properties of the phenomena being Investigated.
This definition may seem daunting but the key take-aways are that Data Science involves techniques used to acquire big data(voluminous, increasing in velocity and with a wide variety), preparing it for use and analysis of the data for extract meaningful insights through statistical analysis. This can then be applied in a business context or specific domain to make data-driven decisions.
The Roadmap
1. Learning the Basics
To kickstart your journey, there are some fundamental concepts that are required as a foundation in learning Data Science. These include
Understanding the nature of Data and different types of data
Mathematics used in computational aspects
Programming and programming languages that work with data such as Python and R
Statistics and Probability which are important in analysis
Machine Learning which is used in modeling of algorithms used in Data Science
These concepts can be accessed through free online resources such as Khan Academy, Freecodecamp and edx among others.
2. Data Collection and Cleaning
Once the foundation is laid, the next important step is learning how to collect data and clean it. Data is obtained from various sources and can be voluminous in nature and thus there is need to know how to collect it, integrate it and prepare it for use in analysis.
For data collection, there are several sites such as kaggle, where datasets can be accessed and downloaded for use. Other methods include web scraping, use of APIs, and databases to collect raw data. Tools like BeautifulSoup and Selenium can help you scrape websites, while APIs provide structured data from various sources. Additionally, one can generate their own data in the case of none.
For data cleaning, techniques such as handling missing values, encoding of data, checking for outliers and consistencies are applied. This ensures high quality and accurate data for use. Tools such as Numpy and Pandas are effective in this task.
3. Exploratory Data Analysis
This is performing analysis on the data to obtain meaningful insights. It involves data visualization and statistical analysis.
Data visualization is the graphical representation of data and can be done using a variety of tools such as Matplotlib, Seaborn and Plotly.
Statistical analysis is a deep dive into identifying underlying patterns and relationships in data to extract meaningful information from it. This can be done through concepts such as hypothesis testing, regression analysis, and correlation among others.
4. Machine Learning
This is the process of selecting and training models to identify patterns from data and use that knowledge to make predictions on new data. Key concepts to benign with are supervised learning, unsupervised learning, reinforcement learning and the machine learning algorithms related to each such as decision trees, logistic regression, clustering etc. There is also need to understand how to assess the performance of the model by using metrics such as accuracy, precision, and recall. Scikit-Learn is an excellent library for this.
5. Practice
To excel and eventually become a data scientist, one needs to practice! Practice! Practice! The journey may be look daunting, but by constantly learning and applying the knowledge learnt in real-world projects available online, the higher the chances of success in your journey as a data scientist.
6. Sharpen your Soft Skills
Aside from the technical skills required, soft skills are also a key requirement in your career as a data scientist. There is need to learn effective communication skills, team work and collaboration, good problem solving skills and analytical skills are also key in your journey to become a data scientist.
7. Stay Up to Date With Trends
Finally, Data science is a continuously evolving field and there is need to keep an out for the latest trends, tools, and technologies by reading blogs, attending conferences, participating in online communities and networking.
Conclusion
Given the tips above, you can now embark on your journey to becoming a data scientist.
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