Data Science is the buzz word right now. Anything to do with it gets trending among the aspirants who wish to carve a career in this field. But why is it getting so much attention from all over?
Well, that is the reason we have come up with this article and throw some light on the topic while bringing you closer to it.
Before we dive deep into it, let us get a basic understanding of the topic with its definition – Data science is defined as an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. It is related to data mining, machine learning and big data.
A sneak peek into the history of Data Science
The subject came into contention in the 1960s when John W. Tukey called for a reformation of statistics. He also shaped engineering and computer science communities. However, DJ Patil and Jeff Hammerbacher are credited with popularizing the term “data science”.
The topic has seen some revolutionary changes in the last 50 years and is likely to get bigger in the near future.
Life Cycle of Data Science
Check out the rundown below to get a sense of the process before we move ahead.
Data capture: It begins with data acquisition, data entry, data extraction and signal reception.
Processing: Then the data is effectively processed with the help of data mining, data clustering & classification, data modelling and data summary.
Maintaining: In this step, the processed data is maintained using data warehousing, data cleansing, data staging, and data architecture.
Communicating: The next step deals with communicating using data reporting, data visualization, business intelligence and decision-making models.
Analyzing: In the final step, the data is analyzed with the exploratory or confirmatory process, predictive analysis, regression, text mining and qualitative analysis.
Data Science is the “sexiest job of the 21st century” – Harvard Business Review.
Data is considered as the new gold mine because of its significance to businesses and that is the reason why the role of Data Scientist has become the most sought after in recent times.
Most of the companies are looking to attract the cream as qualified data science professionals are scarce. According to a survey by the MIT Sloan Management Review, 43 percent of companies report a lack of analytic skills as a key challenge.
Check the stats below:
- Entry Level: 5-7 lakh/annum
- Mid Level: 12-15 lakh/annum
- Senior Level: 21-25 lakh/annum
Note: The displayed figures are just an average of what you can expect and not a guarantee.
- Bachelors degree in Computer Science, Mathematics, Statistics
- Data Science certifications
- Project experience in the field of application.
Expectations from a Data Scientist
Great with numbers
The general perception is that people with a background in maths, stats and science tend to do well as data scientists. You have to be comfortable with numbers as you will be bombarded with plenty of data surrounded by numbers.
Data scientists need to ace the usage of computers and should possess good computer coding skills. Being familiar with technologies like Hadoop or coding languages such as Python is a plus.
Presenting your data analysis graphically to clients is the key. Data Scientists need to simplify complicated and huge volume data for end-users. Graphical representation of the data makes it easier for the audience.
Good data scientists are never tired of asking questions and always dig deeper to discover information that makes the difference. Attention to detail is the key when analysing piles of data.
Data Scientists need to switch on and literally observe something that nobody else can see. After all, that is why they are highly paid. They are responsible for making game-changing decisions.
It does not matter how long and deep data scientists have analysed the data. Clients always expect things to be explained in a simple way. Data Scientists need to exhibit their communication skills to convey their findings in the best possible way.
Apart from finding the crucial details, data scientists need to suggest and recommend further course of actions for their clients. Knowledge of general business practices or a particular industry can set candidates apart.
With more industries and companies realising the benefits and importance of data science, efforts have been made to draft the best talents out there. Many industries have made great progress thanks to the inputs from data science.
Industries like retail, Banking, Automobile among others have seen the best side of big data analytics. Take a look at the image below to understand the hiring patterns of various industries.
Although data science is an attractive profile, it does throw some challenges to the aspirants and professionals who are eager to make their mark.
Big Data and advanced analytics have become a major concern for business leaders across the world for a simple reason – It is going to define the differences between the winners and losers in most of the industries.
“Every company has big data in its future and every company will eventually be in the data business”. – Thomas H. Davenport (Co-founder of the International Institute for Analytics)
Three Key Challenges
There is a tremendous amount of data that is generated by the company. Customer transactions data, internal supply chain data, performance data among other secondary data. Handling such data alone is a big challenge for companies. Determining which data to use, how to source and how to get it together is just the beginning of the task.
This is primarily math-intensive analytic modelling exercise. Getting the right stats, the right people who can use it the right way is another tough exercise.
Digging deep inside the huge piles of data and unlocking much-needed information forms a major challenge.
The third and probably, the biggest challenge arrives while taking all the processed data and transforming the business. It does not make sense to draw vital insights, if you are not going to change the business operates.
If a business is not willing to commit to getting all 3 steps correct – Right data, Right modelling capability, and the right transformational methods then do not start your data journey.
In future everything is going to be connected to the cloud and data which will be mediated by software. If you are ready to face the heat and pull up your socks then buckle up because it is going to be worth it in data science
Can you be a data scientist? Of course, you can, if you….
- Hold a degree in maths, statistics, computer science, management information systems, or marketing.
- Have substantial work experience in any of the above-mentioned areas.
- Are inclined towards data collection and analysis.
- Are comfortable with individualized work and problem-solving.
- Can communicate both verbally and visually.
- Want to broaden your skills and take on new challenges.
Technical skills required to become a data scientist
You should probably make programming language your first language. Programming languages like Python, Perl, c/c++, SQL, R, and Java is a must. Python is the most preferred language in data science roles. Programming languages are essential to clean and organise an unstructured set of data.
Stats and programming go hand in hand when it comes to data science. You should live on numbers and gather plenty of numerical data to facilitate the upcoming processes. Data analysis relies on descriptive stats and probability on a basic level.
Understanding of SAS and other analytical tools
Knowledge of analytical tools is a must in the field of data science. Deep understanding is imperative to extract valuable insights out of primary data. The likes of SAS, Hadoop, Spark, Hive, Pig, and R have made it to the list of most preferred data analytical tools used by data scientists.
Data Extraction, Transformation, and Loading
You should have the ability to extract the right data from sources like MySQL Mongo DB, Google Analytics. Once the extraction is done, the next step is to transform it for storing in a proper structure for detailed analysis. The final step is to load the data in the data warehouse before taking the process ahead.
Data Wrangling and Data Exploration
The raw data ascertained in the warehouse needs to be cleaned and unified. Cluttered and complicated data are then filtered for easy access and analysis. Exploratory Data Analysis (EDA) kick-starts the data analysis process which puts the collected data to use to receive the answers you need.
Machine Learning and Advanced Machine Learning (Deep Learning)
True to its name, machine learning is the process of making machines intelligent. It empowers machines to think, analyze and make decisions. Hands-on knowledge of various supervised and unsupervised algorithms is expected by a data analyst.
Meanwhile, deep learning has elevated the traditional learning approaches to the next level. It stems out of biological Neurons (Brain Cells) which intends to mimic the human brain. Most of the organizations have a soft corner for professionals who have knowledge of Deep Learning and hence that is what you should be taking seriously.
Big Data Processing Frameworks
Big Data uses frameworks like Hadoop and Spark to process huge data (structured and unstructured). Companies use Big Data analytics to gain hidden insights and stay ahead of the competition and hence it is a skill that you should never compromise on.
Data Visualization is one of the major parts of data analysis. It helps data scientists to present their analysis in an understandable and visually pleasing format. It is a skill you have to master in order to stand out from the crowd. Tools like Tableau and Power BI are handy in offering you a nice intuitive interface.
A day in data scientist’s life
We now know what it takes technically to be a data scientist. But what does a data scientist’s professional day out looks like? What keeps them busy day in and day out? To find out, have a glance below and understand the exact nature of work.
- Selecting features, building and optimizing classifiers using machine learning techniques.
- Data mining with the help of state-of-the-art methods.
- Sharing company’s data with third party sources of information whenever necessary.
- Developing data collection procedures to ensure complete collection of data that is necessary to build analytic systems.
- Processing, cleansing, and verifying the authenticity of data used for analysis.
- Doing ad-hoc analysis and presenting accurate results in an understandable manner to end-users.
- Creating automated anomaly detection systems and regular tracking of its performance.
- Understanding and using machine learning techniques & algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Dealing with common data science toolkits such as R, Weka, NumPy, MatLab, etc.
- Using data visualisation tools to prepare analysis and reports.
The Future of Data Science
Data Science is all set to evolve in a bigger way because of its importance. Data Science is without a doubt a rising career. According to Glassdoor, 2016 was the first year in which Data Scientist was the best job on the market. Ever since then it has been the undisputed leader in the professional eco-system.
But why you should be assured of a bright future? Well, because of the basic law of Economics – Supply and Demand. The demand for data scientists is very high, while the supply is too low. A similar scenario is what made computer science popular years ago when the internet was becoming a thing.
As stated in extensive joint research performed by IBM, Burning Glass Technologies and Business Higher Education Forum, the tendency of the gap in supply and demand in data science will continue to be strong for the years to come.
This determines that the salary will be outstanding and consequently, people are looking forward to testing the waters and jump into data science.
Not just that, the fact that data-driven decision making is increasing in popularity underlines the importance of data science and its growth. Every industry is serious about data science and depends on crucial information to make decisions.
So, are you ready to put on your thinking cap and indulge in some data dwelling?