Guide to Learn Data Science and Become a Data Scientist

It is crucial to have a structured learning plan for data science. So, this article will outline the steps to becoming a professional data scientist by focusing on all the key skills required. Let's start step by step!

Step 1: Gaining Proficiency in Python/R programming

Python and R are the preferred languages in the machine learning and data science fields. To be proficient in these areas, it's recommended to know Python, which has a wider community and library support. Companies also expect data scientists to know Python libraries like Pandas (For data reading and processing), Numpy (For mathematical operations on data), and Scikit-learn (for machine learning on data).

Why Python for data science?

• Python is a simple and easy-to-learn language for beginners. Its syntax is clear and concise, which makes it easy to write, read, and maintain code.
• Python has a large number of libraries. Due to this, it is easy to perform data manipulation, visualization, and machine learning tasks using Python.
• Python has a large and active community, and there are many learning resources available.
• Python is a great language for experimenting with ideas. It helps to quickly test and iterate on ideas.

Why R for data science?

• R is designed specifically for statistical computing. It has an easy-to-understand syntax.
• R has a large number of packages available for data analysis, visualization, and machine learning. It has a wide range of visualization tools, which makes it easy to create high-quality charts and plots.
• R has a powerful interactive console, which helps us to explore and manipulate data in real time. This is particularly useful for exploratory data analysis.

Note: Before starting to learn Python or R, we highly recommend understanding the various use cases of both of them in data science.

Step 2: Learning Big Data Technologies like Hadoop and PySpark

Big tech companies like Facebook and Google collect massive amounts of diverse data daily. Traditional methods for processing numerical and tabular databases are insufficient for this task, leading to the emergence of Big Data and associated technologies like Hadoop and PySpark. So, companies in this space require a strong understanding of Hadoop and PySpark.

What is Hadoop?

Hadoop is an open-source framework for processing large amounts of data in a distributed manner across a cluster of computers, which enables it to handle big data efficiently. It was developed by the Apache Software Foundation. The framework has two primary components: the Hadoop Distributed File System (HDFS) and MapReduce.

Hadoop is widely used in data warehousing, log processing, machine learning, and more. Additionally, there are many tools and technologies built on top of Hadoop (Hive, Pig, etc.) that provide higher-level abstractions and enable more efficient data processing and analysis.

What is PySpark?

PySpark is the Python API for Apache Spark, which is a fast and powerful open-source big data processing engine. It helps developers to use Spark with Python.

• PySpark provides a simple interface for working with distributed datasets in Spark and supports popular Python libraries like NumPy and Pandas.
• PySpark offers various features such as distributed SQL queries, machine learning algorithms, graph processing, and more.
• One can develop complex data processing pipelines using PySpark that can be used for various applications like data warehousing, log processing, fraud detection, recommendation systems, etc.

Step 3: Learning the Concept of APIs, Databases and SQL

Data collection and generation are one of the biggest tasks that companies expect every data scientist to know. Data scientists use APIs to collect datasets. Once this dataset is fetched, it needs to be stored somewhere. That's where the need for a database comes in.

Traditional databases like MySQL and Oracle are used to store tabular format datasets, and they are referred to as relational databases. We commonly use SQL for querying and analyzing data in relational databases.

On the other hand, we can use NoSQL databases for structured, unstructured, and semi-structured data. NoSQL databases are also useful for handling high-velocity, high-volume, highly variable data, which may not fit well into the rigid structure of a relational database. Moreover, some NoSQL databases like graph databases are specifically designed to handle complex relationships between data.

So the choice of the database depends on various factors such as the type, volume, desired level of consistency and availability, and specific requirements of the application. One should have a good understanding of various trade-offs related to these databases to master data science.

Step 4: Hands-on Experience in Data Analysis and Visualization Techniques

The more curious we are about data, the more proficient we will be in data science. This curiosity is directly linked to data analysis and visualization. We can analyze the data deeper and extract additional insights. For example, in stock market data, if a data scientist analyzes data and finds a pattern in which the market goes up and down, the company can make an unimaginable profit.

But this requires experience with data, and this is where visualization libraries in Python, like Matplotlib, Seaborn and Plotly can be very helpful. Many companies directly mention these libraries in their required skills section and expect candidates to be proficient in using them.

Step 5: Getting familiar with Statistics, Probability, and Machine Learning

Statistics and probabilities are essential math skills for data scientists. They form hypotheses about the data and validate them using statistical information. If the probability of an event falls below a certain level, the hypothesis is rejected. In particular, a strong understanding of topics such as general probability, probability distribution (continuous and discrete), general statistics, and linear algebra is considered ideal for a data scientist.

Data scientists use machine learning techniques when it is challenging to uncover patterns in data. They feed the machine input and output data, and the machine finds the function that fits it. Machine learning can also solve previously unsolvable problems, particularly those that involve complex data or require high-end operations. With recent advancements, machine learning has become highly valuable and is a sought-after skill for data scientists.

Step 6: Applying ML and Data Science Techniques to Open-source Dataset

Hands-on experience is a must in data science. Earlier, one of the biggest hurdles was the availability of datasets, but nowadays, we can find many open-source datasets on which data scientists can practice their skills. Some of those sources are:

• Kaggle dataset: It is a hub of a wide variety of datasets, including the fields like computer science, environment, agriculture, NLP, and many more. We can easily find complete Machine Learning projects, including data preprocessing and analysis on these datasets.
• Government Datasets: The government publishes data regarding air quality, irrigation percentage, atmospheric conditions, etc. We can use such datasets to build projects like Prediction of soil fertility, Weather forecasting, Probability of rain, etc.
• Toy Datasets from Scikit-learn: Most frameworks these days provide free datasets for learners to practice their skills. The scikit-learn framework also provides toy datasets like IRIS flower type classification, digit recognition, cancer prediction datasets, and many more.

With the help of these datasets, learners can solve some industrial projects to gain experience in relevant skills and algorithms.

Step 7: Make a Resume and Apply for Data Scientist Positions

After completing some projects, it is important to make a detailed resume. A good resume can attract the attention of interviewers and increase the chances of getting shortlisted. Here are some key suggestions:

• List your relevant skills, including Python, SQL, data visualization tools, machine learning frameworks, and other relevant skills like statistical analysis or data mining.
• Provide detailed information on your previous work experience, projects, internships or research that demonstrate your data science skills. Use bullet points to list your responsibilities and achievements.
• Include specific examples of how you have contributed to the success of previous projects.
• Customize your resume for each position you apply for by using keywords from the job description.

After forming and shortlisting your resume, prepare for the interview and start applying for internships or job positions. Openings can be found on platforms like LinkedIn job sections, Indeed, Hirist, TopHire, etc. Please read the job description carefully for the role and try to match your expectations with those of the employer.

Job Descriptions for Data Scientist Roles

We will find three main sections in all the data science job descriptions:

• Job Overview
• Roles and Responsibilities
• Required Skills

Let's understand each of these three fields in detail.

Job Overview

This section summarizes the overall requirement in one or two paragraphs. Sometimes, it also contains information about the project for which they are hiring and what is going to be their work culture. For example, companies providing the facility of remote work (work from home) can mention such benefits in the Job overview. A sample of the job overview is presented below:

As a Data Scientist in XYZ, you will be doing data mining, statistical analysis and scripting to extract relevant data through SQL. You will use the extracted data to find the trends and relevant information. You will also apply various data analytics and ML techniques to a wider domain of business problems linked with data.

Some traits we are expecting in the candidate are:

• A team player
• A collaboration champion
• Comfortable being uncomfortable
• Open for feedbacks
• A problem solver
• Comfortable with multiple projects
• Business and tech-curious

Roles and Responsibilities

This section lists all the relevant tasks for which the company is hiring. It is the most crucial section in any job description as it gives us a sense of our job and the tasks we will be doing if we are selected. If some tasks do not match our interests, we can discuss them in the interview. For a data scientist position, a sample of the roles and responsibilities is shown below:

• Collecting and interpreting data
• Defining new methods for data collection and analysis
• Building machine learning models to predict user trends
• Analyzing user behavior and continuously improving the model with new data
• Presenting results with visually appealing techniques
• Conducting thorough business hypothesis testing and verifying the hypothesis with data
• Working with business analysts and data engineering teams to achieve goals.

Required Skills 

Every job, whether entry-level or experienced, demands a certain level of skillset. If the job is entry-level, employers demand our educational background or academic project experiences be inlined with their expectations. For professional positions, they expect candidates to come with proven work experience in data science. This section also mentions the qualifications/degrees we should have to apply for the particular position.

For a data scientist position, a sample of the required skills from a job description:

• B.S./B.Tech/M.S./M.Tech degree in Computer Science, Electrical Engineering or a related field.
• Good experience in Python and familiarity with libraries such as Pandas, Numpy, and Scikit-learn.
• Deep understanding of both supervised and unsupervised machine learning algorithms, including classification, clustering, and regression.
• Good knowledge of Big Data processing using Hadoop and Spark.
• Familiarity with Git, Flask, and REST APIs.
• Understanding of A/B testing and hypothesis testing.
• Solid understanding of the tools and techniques necessary for data analysis and pre-processing.

This summarizes any job description for the Data Scientist role. The reason for explaining the job description for these roles is to make learners aware of what is required to become a Data Scientist.

Conclusion

Data Science is a rapidly growing career path, with many companies amassing large amounts of data and seeking Data Scientists for roles such as model building, data analysis, data preprocessing, data engineering, and more. This article provides a 7-step guide to becoming a successful Data Scientist and securing a career in the field. We hope you find the information valuable and engaging.

If you have any queries or feedback, please write us at contact@enjoyalgorithms.com. Enjoy learning, Enjoy data science!