Data Science is one of the most lucrative or tempting areas to work in recent years for computer scientists. The area of Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, Neural Network, Deep Learning, and all related fields are growing at an astronomical speed and creating uncountable jobs with unbelievable salary.
If you want to know more about AI and its application areas, check my other post here dedicated to AI.
At the end of this post, you will get a good idea about what is data science, what type of jobs you can do, and what is the salary package in the USA or other countries. Also what prerequisite skills you need to get into this field of work.
If you don’t take the initiative to learn these technical skills and take advantage of this technology trend, then you are probably missing an opportunity for the big time. Soon there will be a shortage of skilled people in this field and more number of available jobs.
Table of Contents
What is Data Science ?
Data Science is a special field in Computer Science which mainly focuses on Data. It requires knowledge of Mathematics, Statistics, and expertise in a particular domain.
The job of a data scientist is to record the data, store the data ( both structured or unstructured ), analyze them using different satistical analysis formulae, and help make predictions or decisions.
The analysis is also based on knowledge of that particular domain/subject, where we conduct the analysis ( e.g. Weather Forecast, Energy Management, etc) and get a meaningful insight into that data.
Why do we need Data Science?
When machines ( e.g. A car or A refrigerator or wind-mill ) are on and working on their full capacity, they create a huge amount of data ( e.g. in the form of electrical signals ). Earlier it was a challenge for scientists to store that large data set. Eventually, the concept of Big Data has solved that problem.
Once we collect and store the data ( unstructured / semi-structured ) into large data set using big data technology e.g Hadoop, we apply data science techniques to get an insight into different parts of that data. Some technology e.g. Several organizations use python a programming language to analyze that data ( using its wonderful variety of libraries) to get meaningful information in terms of charts, graphs, etc. This helps to understand certain patterns of that data and can be extremely important to make business decisions.
Let’s understand it better
For example, let’s say a large chain of retail store e.g. Walmart. It generates a huge amount of data about its customers. Which demographic they belong to, the majority of them buy which type of products, what time they generally visit the stores, where do they live, the log files of their business transactions with Walmart, etc. There are many more complex types of data, which are either semi-structured or unstructured but the amount is huge.
Walmart collects these data and stores them in Big data. Data Scientists apply statistical analysis methods on the data ( guided by the knowledge of a subject expert in E-commerce, Retailing domain ), to get insights about some patterns of customer’s buying behavior.
It can be extremely important to make business decisions based on that analytics, which will impact companies overall sales volume e.g. creating automatic recommendations for the customers by sending notifications through smartphone apps or emails.
When new products arrive at the store which is similar to previously bought products, customers get alerts in their smartphone apps.
Technical Skills needed to become a data scientist ?
To pursue a career as a data scientist, the preferred degree is a Ph.D. in either Data Science, Statistics, or Computer Science. Companies prefer to hire candidates as data scientists with a Ph.D. degree. This has been kind of an industry-standard so far. Having a Ph.D. provides you with a core foundation about the technical aspects of Data Science.
But there are people who can’t afford this degree for various reasons but this technology interests them There are short term certification courses available which will help you to work in the industry as a data scientist.
You have to learn one of the programming languages to implement data science techniques. To mention a few PYTHON, C, C++, PERL, JAVASCRIPT.
Other than technical skills it is always an added advantage to have communication skills, understanding of how business works, and data intuition skills.
Behavioral Trees in Robotics and AI
A Behavior Way or (BT) is a way to structure the switching between
tasks of an artificial or autonomous agent such as a robot or non-player
an entity like a robot in a computer game.
more info
What is Big Data ?
Big data refers to a massive chunk of data. This can be either structured or unstructured. The size of big data is usually in petabytes or exabytes and they are used to store millions of records e.g as I mentioned earlier for Walmart, these records contain information about customers, their phone numbers, social media, mobile data, email address, transaction details.
It is not possible to manage that data using traditional database systems ( relational databases such as Oracle, SQL Server, etc ). One of the most popular Big Data used in the industry is Hadoop.
What is Big Data Analytics ?
This is a quantitative and qualitative analysis of data to get Meaningful Insight. The following are the different analytics types.
- Prescriptive Analytics: Predictive Analysis involves roles and recommendations. Based on the predictions it recommends what to do at the next level. It tells what action to take using analysis, AI, Neural network, etc. This analytics involves the most advanced type of data analysis compared to others.
- Predictive Analytics: Predictive Analysis uses data modeling and statistics to make future predictions. From the pattern of the current and past data, this system predicts whether a similar situation will happen again or not. Data mining plays an important role in Predictive Analytics.
- Diagnostic Analytics: We use this type of Analytics to find the cause of the event happened. e.g. A shipment company is trying to find out the reason for the delayed shipment of goods. So if the root cause is identifiable for a certain data pattern then this type of data analysis eliminates repetitive work when a similar event happens in the future.
- Descriptive Analytics: This is the most basic and simplest type out of all four types of data analytics. Descriptive analytics represents data in the form of dashboards, KPI, etc and the main purpose of this analytics is to identify the ” root cause ” of the event.
Applications of Data Science ?
Targeted Advertising
The scope of data science is huge. The following are some of the most important areas where data science is a must. Online advertising is a multi-billion dollar industry. Most of the Social Media Companies take advantage of that and that’s their main way of making revenue.
Following are the different ways to implement targetted advertising.
- Targeting based on particular demographics: This type of targeting is based on age, ethnicity, gender, salary, location, etc.
- Targeted Ads based on particular categories of products: If people are interacting on a website that is promoting or selling a particular product/ service let say washing machine, then more ads of similar products will be displayed on that website.
- Targeting based on particular behavior: Sometimes companies place targeted ads based on the behavioral pattern of the customers. The parameters are site clicks, site visits, searching for particular items online, etc.
One of the most important ways of doing targeted ads is RTB ( Real-time bidding ). There are websites that do auctions and allowing advertisers to put ads in real-time.
Image and Speech recognition:
Data science plays a very important role in image and speech recognition. Some organizations ( e.g. banking system ) use these techniques to identify their customer.
Image recognition technology is used to validate the customer’s face ( also known as face recognition). This is done with the help of AI, neural networks, and pattern recognition techniques.
For more details on AI and its application areas, check my other blog HERE !
Nowadays attackers are using disruptive technology to break into the system. Therefore organizations are putting more and more security constraints for making these applications more secured.
Fraud detection
Data science is a lifesaver for banks, financial institutions, retail companies, etc. The most common frauds we generally notice and handled by Data Science technology, are mentioned below.
Tax return:
A faulty tax return is a problem that costs IRS billions of dollars and it prevailed in the system for years. Thanks to data science which has detected anomalies in tax filing using its predictive analytics s where based on the previous year’s tax filing it will figure out what should be the expected tax return.
Credit/debit card transactions
Any abnormal behavior that happens in credit/debit Financial institutions can stop unlawful credit/debit card transactions and protect customers identity from being stolen using the help of data science e.g. if someone stole or using another person’s credit/debit card unlawfully, the system can detect it and necessary actions can be taken to stop further transactions. The alert notifications system which we receive from the bank time to time in our phone, are nothing but comes from its predictive analytics, powered by AI, Machine learning, Neural networks which are used for recognizing the patterns of customer behavior.
Faulty Item return:
Many Big retail chains are facing millions or even billions of dollars of loss per year because of their customer-friendly return policy. Some people are taking advantage of that and making unlawful returns of items that they might not have bought. Retail companies are solving these problems using data science analytics.
Health Care Fraud:
Many times we notice In the healthcare system that patients get the wrong medicines. The diagnosis went wrong and the surgery cost coming as huge. There is definitely some problem with the diagnosis. Either its wrong or some fraud is happening. To identify this, Data Science is very important based on previous data about the patient’s health-related records. Data analytics on patient’s health can identify any future health problem thereby saving unnecessary treatment saving costs.
Salary of a Data Scientist ?
Demands for data scientists are increasing every day. There are three different levels of data scientist job profile.
Entry Level Data Scientist Job
The average salary of entry-level data scientist jobs is between $90 k – 100 k.
Mid Level Data Scientist Job
The average salary of mid-level data scientist jobs is between $120 k – 130 k.
Experienced data scientist Salary
The median salary of an experienced / professional data scientist ranges between 150 k-$200 k USD
Some Important Links for referral
- Here is an important article I have found on Data Science.
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