Introduction

Data Science involves collecting organizing and analyzing raw data from various sources and deriving insights from this data that will help an organization make informed decisions. These decisions aim to improve efficiency, boost profitability and fuel growth.

Why Data Science

If you find yourself to be curious, with an analytical mind, then Data Acsience could be for you.

Data Science is one of the fastest-growing industries around the world, with the U.S. Bureau of Labor Statistics (BLS) estimating a 22% growth in Data Science job creation through 2030. The salary ranges too, are very attractive, with an average of $103,903, or $8,659 per month, according to BLS. Nearly every industry and organisation, cutting across governments, retail, and healthcare, requires data scientists.

Data Science Life Cycle and Skills Required

As much as every data science project is unique, depending on the problem and industry, most will follow a similar life cycle.
There are typically 6 steps in the data science life cycle are as below:

Problem Understanding

Also referred to as business understanding, involves understanding the organisation, defining a problem identified by the organisation, objectives to be achieved and constraints.

• Skills: Strong business acumen, communication, and problem-solving abilities.
• Technologies: The focus here remains on understanding the business objectives.

Data Collection and Exploration

Gather relevant data from various sources, such as databases, Excel files, text files, APIs, web scraping, or even real-time data streams. The type and volume of data collected largely depend on the problem you’re addressing. The data is then stored appropriately for further processing.

• Skills: Data querying (e.g. SQL), data cleaning, and exploratory data analysis (e.g. EDA).
• Technologies: SQL, Python (pandas, NumPy), R.

Data Cleaning and Preprocessing

This is most often the most time-consuming step of the life cycle. It involves cleaning messy data, handling missing values, transforming features and preparing data for modelling. The objective is to create a clean, high-quality dataset that will yield accurate and reliable analytical results.

• Skills: Data wrangling, feature engineering, and statistical knowledge.
• Technologies: Python (pandas, scikit-learn), R.

Data Analysis and Modeling

You explore the prepared data to understand its patterns, characteristics, and potential anomalies. Apply machine learning algorithms (regression, classification, clustering) to build predictive models.

• Skills: Statistical analysis, machine learning, model selection.
• Technologies: Python (scikit-learn, TensorFlow, PyTorch), R.

Deployment and Maintenance

This involves interpreting and communicating the results derived from the data analysis. You will communicate your findings to stakeholders effectively, using clear, concise language and compelling visuals using tools like Tableau or PowerBI. The goal is to convey these findings to non-technical stakeholders in a way that influences decision-making or drives strategic initiatives. If satisfactory you will deploy the models into production, monitor performance and update as needed.

• Skills: Software engineering, cloud deployment, monitoring, communication (storytelling).
• Technologies: Docker, Kubernetes, cloud platforms (e.g. AWS, Azure, GCP.

Point to note:

You may have to go back in the cycle, at any step, before completion if you find that the models do not perform as expected. That is why the data cleaning phase consumes a lot of time, over 50%, and is considered one of the most important steps in the cycle.

Career Path for a Data Scientist

Apart from being curious and analytical, there are a range of skills that you need to develop to progress in this field.
There are various levels one will go through as a Data Scientist, as you gather experience and skills along the way, each applicable in the Data Science life cycle.

• Junior Data Scientist: You will be working mainly on the basics of data analysis, like extracting, cleaning, integrating and loading data. They will often use pre-existing models, and work within specifications set by senior Data Scientists. Typically you will get about 2-3 years experience at this level.
• Mid-Level Data Scientist: You will perform more exploratory analysis, and build statistical models to solve problems. You may also work with senior data scientists in machine learning and AI.
• Senior Data Scientist: You may be here after 3 to 7 years. You will be putting models to work in conjunction with other advanced tools. You will monitor and fine-tune the organisation's methodologies, and collaborate with other organisational stakeholders in communicating the relevant insights.
• Data Science Managers: Here you typically have at least 5 years prior experience as a Data Scientist, with about 2-3 years in a supervisory role. Yours is to hire the right people, set realistic goals and KPIs, create a productive environment and ensure your organisation remains competitive by being aware of changing developments within the industry.

Across various industries, the job growth for data scientists is on the rise. With the increasing availability and affordability of the Internet and the growth of the "Internet of Things", the need for data scientists will continue to grow.

Consider the below examples of industries where Data Science is applicable:

• Finance: Fraud detection and mitigating risks.
• Health: Health applications, disease tracking and management.
• Transport: Managing traffic and Autonomous Vehicle (AV) development.

Begin Your Journey as a Data Scientist

Start Learning Key Data Science concepts and tools

You will need to start to understand the fundamental concepts of data science. Get familiar with statistics and mathematics.
You need to learn to code as an essential skill as well. SQL, Python and R are the most commonly used.
Get familiar with machine learning as well. Finally, you need to be familiar with visualisation techniques and tools like Tableau or PowerBI.

Develop a Portfolio

Hands-on experience is crucial in this field. You can start working on small personal projects and documenting them in a portfolio. This helps improve your skills. A strong portfolio demonstrates your creativity and practical skills.

Lean On your Networks

Connect with professionals in the field through social media, meetup events/hackathons and communities, and learn from each other. Many jobs have come as a result of these through referrals.
Get yourself a mentor coach, who can offer guidance, give you insights on the industry and help you develop a resume.

Stay Curious And Keep Learning

Curiosity is one of the essential soft skills you will need as well. You will also need to keep abreast with industry standards and developments, and tools in use.

Conclusion

Data continues to be deeply embedded in our lives, and we are experiencing exponential growth in the availability of big data.
Data Science is at the forefront of giving meaningful insights for data-driven decision-making across various industries. Now is a good time as any to start your journey in the world of data as a Data Scientist where many opportunities await you.