Many wonder what is data science. Is it the power to transform truckloads of data and knowledge into consumable information that will, therefore, predict future trends? It sure is, and today it has evolved to a huge extent. The study involves analysing data, data mining and use of various programming languages. Graduates study math and statistics, programming, artificial intelligence and machine learning in this regard. These will guide the individual into a sure data scientist of the 21st century. So read through our blog as we “analyse” important data and help you digest, nourish, and finally flourish in your future career!
History Of Data Science
Data Science traces its roots back to the 1960s to 1970s when computer science and the study of statistics began to come together. This was when the actual importance of computers was realised by statisticians and engineers. This set them in motion to design and develop specific tools to analyse this data. SQL and regional databases were game changers and propelled us into a new era of data interpretation. These are some key milestones which you should know of:
- The marriage of statistics and computers was registered, and this leveraged powerful mainframe computers to perform complex statistical analysis.
- Databases like SQL came to power which was considered a revolutionary moment. For the first time. Big MNCs could systematically store and retrieve data in a respectful manner.
- Researchers Auther Samuel and Frank Rosenblatt planted the seed of Machine Learning, a cornerstone of data science, in the 1950s and 1960s.
- The decade of the 80s was when data warehousing technology turned heads of millions. This allowed companies to store an consolidate data in a more structured and centralised repository than in the previous decade.
What Is Data Science? Vs Who Are Data Scientists?
The term ‘data scientist’ was put together when MNCs realised the need for bouts of data to be parsed into neat organisation. No sooner than 10 years into the adoption of the internet on a widespread level, Google’s chief economist – Hal Varian, stressed on how moving with the speed of technology is vital for us.
He famously said, “The ability to take data, to be able to understand it, to process it, to extract value from it, to visualise it, to communicate it – that’s going to be a hugely important skill in the next decades”.
Hal’s words rang true. Today, we see data scientists integrated into the nitty gritty of every organisation. They identify questions, collect data from several resources, and organise the information to produce solutions that will benefit the organisation’s business.
Data Science Vs Business Intelligence
Business Intelligence (BI) analyses historical data to understand past trends and performance of the company. Therefore, it enforces data-driven decision-making in the organisation.
- On the other hand, when we look at data science, we see that it aims to understand and translate present data to predict future outcomes for the organisation. It makes note of patterns and prescribes actions which might be fruitful for the organisation.
- BI deals with unchanging structured data, whereas data science can handle both structured and unstructured data.
- BI tools tell you about what happened in the past, whereas Data Science has the ability to forecast future events.
- Business Intelligence uses technologies such as data warehousing, data mining and data visualisation, and in Data Science we have machine learning, deep learning and artificial intelligence.
- Professionals involved in BI of a company require skills in data analysis, reporting and visualisation. On the other hand, data scientists are ultimate experts of programming, statistics, machine learning and domain knowledge.
Key Concepts In Data Science
The concepts mentioned in this section of our blog further help you understand what is data science. From data acquisition to model building and deployment. Here are some key areas which you should know of:
- Big Data and Cloud Computing – The ability to store and process massive amounts of data into cloud platforms which will help data scientists to make better decisions.
- Exploratory Data Analysis (EDA) – This involves understanding the characteristics of the data and using visualisation techniques such as scatter plots, histograms, box plots and others.
- Machine Learning – This has allowed data scientists to build more accurate models, automate tasks and improve predictions.
- Business Intelligence – Companies have successfully made use of data science to predict analytics and business intelligence. Thus, trying to find that edge over others hugely increases the pool for competition.
- Data Visualisation and Communication – Data scientists make dashboards and interactive visualisations to communicate the right insights to stakeholders.
What are popular Tools used in Data Science?
These are some open-source languages which are incredibly versatile for statistical modelling, machine learning and data visualisation:
- R and Python: These are two widely used languages in the data science field. RStudio is an environment that’s made for statistical computing and graphics. It’s a go-to choice for data scientists! Python on the other hand comes loaded with libraries like NumPy, Pandas and Matplotlib.
- SAS: This is a panoramic suite that offers almost everything from data visualisation to predictive modelling.
- IBM SPSS: This one is known for its statistical analysis style. It goes heavy on, extensive machine learning algorithms and big data platforms.
Other Tools Used
Big Data Processing Platforms:
- Apache Spark
- Apache Hadoop
- NoSQL databases
Data Visualisation Tools:
- Business Applications (Microsoft Excel)
- Commercial Tools (Tableau, IBM Cognos)
- Open-source tools (DS.js, RAW Graphs)
Machine Learning Frameworks:
- PyTorch
- TensorFlow
- MXNet
- Spark MLib
What Is Data Science Process?
The process of data science starts when a business problem arises. This is solved with data scientists and business stakeholders working closely to understand the needs and requirements of the problem. Once the issue and its roots are identified, the team of data scientists go head-on and tackles the problem using the OSEMN process:
O – Obtain Data
Data scientists extract data from sources like company databases, CRM software, web server logs, and social media, or they even purchase it from third-party vendors.
S – Scrub Data
Data Scrubbing, as it is called, involves characterising the data according to its format. A crucial step here is to handle missing data, fix errors in it and remove outliers.
E – Explore Datase
In this step, data scientists plan for further steps using modelling strategies. The use of various tools helps them to gain initial understanding. Furthermore, it explores and identifies patterns that need deeper study or action.
M – Model Data
Software and machine learning techniques are deployed to get insights, predict after-effects and prescribe the right actions. These techniques also include association, classification and clustering which will thereafter assess the accuracy of the model.
N – Interpret results
The last step is the translation of insights into actionable plans. On the basis of their discoveries, diagrams, graphs and charts are made. Business stakeholders can now implement the results of the recommendations made by data scientists.
Application Of Data Science In Different Industries
Data science has various applications and is used in various industries today, including business. Some of the most popular ones are listed below:
| Applications | Applications | Examples |
| Healthcare | Predictive modelling for patient outcomes and treatment efficacy | Identifying patients at risk of readmission by analysing patient demographics, medical history, lab results and vital signs |
| Medical imaging analysis | Using deep learning algorithms to analyse medical images such as CT scans and MRI to diagnose a disease. | |
| Finance | Fraud detection | Identifying suspicious transactions and patterns using machine learning algorithms and anomaly detection techniques |
| Portfolio Optimisation | Scrutinising investment portfolios using methods like mean-variation and Monte Carlo optimisation. | |
| Customer segmentation and marketing | Identifying customer segments and targeting those marketing campaigns using clustering and decision trees. | |
| Retail | Marketing and recommendations | Analysing customer data to make personalised product and content recommendations |
| Demand Forecasting | Forecasting sales and demand using real time series and machine learning methods. | |
| Inventory Management | Optimising inventory levels using forecasting and other optimisation methods. |
Top Universities In The World
Data Science has grown to become one of the most pursued and chosen careers by students this year. A report published by ‘The National Center for Education Statistics’ said that there was a 968% jump in data science masters in the USA. There are many universities around the world which tap into this field of study. Read about this below:
| Name Of The University | QS Ranking 2024 |
| Massachusetts Institute of Technology | #1 |
| Carnegie Mellon University | #2 |
| University of California, Berkeley | #3 |
| University of Oxford | #4 |
| Harvard University | #5 |
| University of Toronto | #6 |
| University of Washington | #7 |
| Princeton University | #8 |
| EPFL | #9 |
| Georgia Institute of Technology | #10 |
Growth In Demand For Data Science
Employment of Data Scientists in the US is projected to grow a whole 35% from 2022 to 2032. On average, openings will increase by 17,000 each year. Have a look at the below professions and their base salaries:
| Name Of The Scientists | Average Annual Salary |
| Data Scientist | $152,279 |
| Data Analyst | $80,109 |
| Data Engineer | $115,472 |
| Data Architect | $155,022 |
| Machine Learning Engineer | $151,961 |
| Business Intelligence Engineer | $132,593 |
Top Companies Hiring Data Scientists
Data Scientists are being employed all over the world like never before. Dice’s recent Tech Salary Report says that their base salary pins down to $117,241 per year. The table below has the names and salaries of the top companies employed today!
Challenges Which Data Scientists Face And Ways To Overcome Them
Now that you know what is Data Science, it is important to also understand the challenges Data Scientists face. Here we have highlighted some of them:
Challenge 1: Finding the right data
Locating and accessing the right data takes a huge amount of time. Since organisations produce vast amounts of data, finding the right dataset might take considerable energy and time.
How To Overcome This:
- You can start by creating a data repository which provides metadata and context for each dataset.
- Also, ensure that there is proper data documentation and regular maintenance of these resources.
Challenge 2: Data Quality & Cleaning:
Real-world data is messy and incomplete. To deal with such resources, data cleaning is the right thing to do. This process is often time-consuming but at the same time necessary for ensuring high-quality data.
How To Overcome This:
- Use of data cleaning tools may come to use here. This will help identify and correct errors and fil in missing values. Also, this will remove duplicates.
- The use of these tools will also help save time and allow you to focus on the more complex part of data cleaning.
Challenge 3: Handling Large Volumes of Data
Handling and processing these whopping chunks of data may be the next challenge you face. Traditional data processing tools may not be able to take up such a mammoth task.
How To Overcome This:
- Advanced tools and technologies such as Hadoop and Spark are designed for such situations.
Challenge 4: Balancing Data Security
Data security is a critical concern when handling sensitive information. Data Scientists need access to confidential data but making certain that proper data regulations are applied can be challenging.
How To Overcome This:
- Apply data governance policies and practices. These include data access controls, data encryption and data anonymisation.
- The use of data catalogues to manage data access can be helpful as well. Here, you can restrict access based on user roles and permissions.
Challenge 5: Communicating Results to Non-Technical People
Data Scientists find it extremely hard to communicate their findings in simple terms. Poorly defined business terms and KPIs can also make it difficult for them to understand and explain the impact of their work.
How To Overcome This:
Practicing storytelling might help! Learn to present your findings in a narrative and visualised manner to convey the value of what you’ve worked on.
Data Science Use Cases
One way Data Science can be useful to companies is through something called “intelligent automation”, this means using technology to automate tasks in a smart way. Another way Data Science helps companies is by improving their customer experience. This is done through something called “enhanced targeting and personalisation”. Basically, Data Science helps companies understand their customers better.
Here are some other examples of how companies are using Data Science:
- A bank created a mobile app that uses machine learning to quickly decide if someone should be approved for a loan or not.
- A company that provides automation solutions created a system that can understand the content and tone of customer emails. This can help the service teams prioritise the most important and urgent emails.
- A company that makes electronics is working on creating really powerful sensors for self-driving cars. Data Science helps these sensors better detect objects in real-time.
- A media company created a platform that uses data analytics and machine learning to understand what TV shows are engaging audiences the most.
- A police department created tools that use data analysis to help officers decide where to deploy their resources to prevent crime. These tools provide dashboards that give officers a better understanding of the situations at hand.
- A medical company used artificial intelligence to create a system that can analyse medical records and categorise patients based on their risk of having a stroke.
Future Of Data Science
In the near future, many of the tasks currently done by data scientists could be potentially automated by AI and other tools. However, data scientists will be needed to oversee these automated systems, monitor their outcomes and interpret the results.
Social media and other websites will become major sources for collecting data about people’s thoughts, opinions and behaviours. Companies can use this data to develop new products and marketing strategies that better match what customers want.
The big challenge is figuring out how to use data science models to drive real business decisions and actions. Simply building models is not enough – they need to be operationalised in a particular way.
As more data moves towards cloud, data scientists will need skills in cloud computing, cybersecurity and protecting data from threats. Their coding abilities will become even more crucial as tools grow more advanced.
Finally, while AI may automate some tasks, the data scientist’s role itself will grow and evolve.
FAQs
What is Data Science?
This is a field of study which combines statistics, mathematics, computer science and domain knowledge.
Which are the top universities offering data science in Australia?
Some of the famous data science institutes in Australia are the University of Melbourne, the University of Sydney, Monash University and the University of New South Wales.
What is the scope of studying data science in Ireland?
MS in Data Science in Ireland brings in opportunities to work at European headquarters of Amazon, Facebook, Microsoft and other MNCs. Moreover, Irish universities are well-known for their postgraduate and undergraduate programmes in data science, data analytics and related fields.
Should I go for a data science masters from the UK?
Masters in Data Science in the UK because the country has some of the top universities housing this subject. These universities go on to offer students opportunities to engage with professionals deployed in this field.
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I have done Bachelor’s in Culinary Arts from India and completed my graduation in the year 2022 .I am 22 years old. After graduation, I have done 1 year paid internship from USA .Now, I would like to take occupational experience and learn culinary skills and also do masters in Culinary arts.How can I find the college n best course / country where I can persue studying further