Why Data Scientists are in Demand?
In this article, I am going to discuss Why Data Scientists are in Demand. Please read our previous article, where we discussed Hadoop Ecosystem. At the end of this article, you will understand Why Data Scientists are in Demand and where Data Science is used.
Why Data Scientists are in Demand?
As the world entered the era of big data, so did the demand for data storage. Until 2010, it was the primary challenge and concern for the enterprise industries. The primary focus was on developing a framework and data storage solutions. Now that Hadoop and other frameworks have successfully solved the storage problem, the focus has shifted to data processing. The secret sauce here is data science. Data Science can make all of the ideas that you see in Hollywood sci-fi movies a reality. The future of Artificial Intelligence in Data Science. As a result, it is critical to understand what Data Science is and how it can benefit your business.
Data Science is a collection of tools, algorithms, and machine learning principles that aim to uncover hidden patterns in raw data. But how does this differ from what statisticians have done for years? The answer is found in the distinction between explaining and predicting.
As shown in the preceding image, a Data Analyst typically explains what is going on by tracing the data’s processing history. A Data Scientist, on the other hand, not only performs exploratory analysis to glean insights from it but also employs a variety of advanced machine learning algorithms to predict the occurrence of a specific event in the future. A Data Scientist will examine the data from a variety of perspectives, including some that were previously unknown.
As a result, Data Science is primarily used to make decisions and predictions by utilizing predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.
Predictive Causal Analytics:
If you want a model that can predict the likelihood of a specific event in the future, use predictive causal analytics. For example, if you lend money on credit, you are concerned about your customers’ ability to make future credit payments on time. Here, you can create a model that can perform predictive analytics on the customer’s payment history to predict whether or not future payments will be made on time.
If you want a model that has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes. The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to make decisions like when to turn, which path to take when to slow down or speed up.
Machine Learning for Prediction:
If you have transactional data from a finance company and need to build a model to predict future trends, machine learning algorithms are your best bet. This falls under the supervised learning paradigm. The term “supervised” refers to the fact that you already have data on which to train your machines. A fraud detection model, for example, can be trained using a historical record of fraudulent purchases.
Machine Learning for Pattern Discovery:
If you don’t have the parameters from which to make predictions, you must discover hidden patterns within the dataset to make meaningful predictions. Because there are no predefined labels for grouping, this is an unsupervised model. Clustering is the most commonly used pattern discovery algorithm.
Assume you work for a telephone company and need to build a network by erecting towers throughout a region. Then, using the clustering technique, you can locate those tower locations that will ensure that all users receive optimal signal strength.
Data Science Skills
Data science is an umbrella term that encompasses data analytics, data mining, AI, machine learning, Deep Learning, and several other related disciplines. The majority of organizations have now recognized the significance of data-driven decision-making. Before I go any further, here is a list of Data Scientist skills that will get you hired:
- R/ Python – at least one programming language
- Extraction, Transformation, and Loading of Data
- Data Manipulation and Data Exploration
- Algorithms for Machine Learning
- Machine Learning at its Finest (Deep Learning)
- Frameworks for Big Data Processing
- Visualization of Data
Let me categorize the skills before I explain each of the above-mentioned points. As a Data Scientist, you will be in charge of jobs that span three skill sets. Statistical/mathematical reasoning, business communication/leadership, and programming are all examples of skills.
According to Wikipedia, statistics is the study of data collection, analysis, interpretation, presentation, and organization. As a result, it should come as no surprise that data scientists must be familiar with statistics.
Data analysis, for example, necessitates at the very least descriptive statistics and probability theory. These ideas will assist you in making better business decisions based on data.
2. R/ Python Programming Language:
Using a programming language, you can manipulate data and apply algorithms to derive meaningful insights. Python and, are two of the most popular programming languages among data scientists. The primary reason is a large number of Numeric and Scientific computing packages available. Machine Learning Algorithms can be easily applied using Python packages such as Scikitlearn and R packages such as e1071, rpart, and so on.
3. Extraction, Transformation, and Loading of Data:
Assume we have multiple data sources, such as MySQL DB, MongoDB, and Google Analytics. You must extract data from such sources and then transform it for storage in a suitable format or structure for querying and analysis. Finally, you must load the data into the Data Warehouse, where it will be analyzed. As a result, for those with an ETL (Extract, Transform, and Load) background, Data Science may be a viable career option.
4. Data Wrangling and Data Exploration:
You have data in the warehouse, but it is inconclusive. As a result, Data Wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis. The first step in your data analysis process is exploratory data analysis (EDA). Here, you make sense of the data you have and then decide what questions to ask and how to frame them, as well as how to best manipulate your available data sources to get the answers you need. This is accomplished by taking a broad look at patterns, trends, outliers, unexpected outcomes, and so on.
5. Machine Learning and Advanced Machine Learning (Deep Learning):
As the name implies, machine learning is the process of making machines intelligent, with the ability to think, analyze, and make decisions. An organization has a better chance of identifying profitable opportunities – or avoiding unknown risks – if precise Machine Learning models are built. You should have hands-on experience with a variety of Supervised and Unsupervised algorithms.
Deep Learning has elevated traditional Machine Learning approaches to new heights. It is based on biological Neurons (Brain Cells). The goal here is to simulate the human brain. Deep Neural Networks are constructed from a large network of such Artificial Neurons. Nowadays, most organizations require knowledge of Deep Learning, so don’t pass this up.
Python is the most popular programming language among Machine Learning experts, and TensorFlow is one of the most well-known Python libraries for developing Deep Learning Models.
6. Big Data Processing Frameworks:
Training Machine Learning/ Deep Learning models necessitates a massive amount of data. Creating precise Machine Learning/ Deep Learning models was previously impossible due to a lack of data and computational power. Nowadays, a large amount of data is generated at a rapid pace. Because this data can be structured or unstructured, traditional data processing systems cannot process it. Big Data refers to such massive data sets.
As a result, frameworks such as Hadoop and Spark are required to handle Big Data. Nowadays, most businesses use Big Data analytics to uncover hidden business insights. As a result, it is a necessary skill for a Data Scientist.
7. Data Visualization:
One of the most important aspects of data analysis is data visualization. It has always been critical to present data in a clear and visually appealing manner. Data visualization is one of the skills that Data Scientists must learn in order to communicate more effectively with end-users. There are numerous tools available, such as Tableau and Power BI, that provide a user-friendly interface.
Data Science Use Cases (Why Data Scientists are in Demand)
1. Facebook – Using Data to Transform Social Networking and Advertising
Today, Facebook is the world’s social-media leader. With millions of users worldwide, Facebook conducts large-scale quantitative research using data science to gain insights into people’s social interactions. Facebook has evolved into an innovation hotspot, employing advanced data science techniques to study user behavior and gain insights to improve its product. Deep learning, a cutting-edge data science technology, is used by Facebook.
Facebook uses deep learning for facial recognition and text analysis. Facebook uses powerful neural networks to classify faces in photographs for facial recognition. To understand user sentences, it employs its own text understanding engine known as “DeepText.”
It also makes use of Deep Text to understand people’s interests and to align photographs and texts. However, Facebook is more of an advertisement corporation than a social media platform. Deep learning is used for targeted advertising. It uses this to determine what types of advertisements users should see.
It uses the data insights to group users based on their preferences and serves them advertisements that are relevant to them.
2. Amazon – Using Data Science to Transform E-Commerce
Amazon has worked hard since its inception to become a customer-centric platform. To increase customer satisfaction, Amazon heavily relies on predictive analytics. It accomplishes this through the use of a personalized recommendation system.
This recommendation system is a hybrid type that also includes comprehensive collaborative filtering. Amazon analyses the user’s previous purchases to recommend more products. This is also reflected in the suggestions made by other users who use similar products or provide similar ratings.
Amazon has an anticipatory shipping model that uses big data to predict which products its users are most likely to buy. It analyses your purchasing patterns and sends products to your nearest warehouse that you may use in the future.
Amazon also optimizes prices on its websites by taking into account a variety of factors such as user activity, order history, competitor prices, product availability, and so on. Amazon uses this method to provide discounts on popular items while earning profits on less popular items.
3. Uber – Using Data to Improve Ride Quality
Uber comes in second in terms of data science use cases. Uber is a popular smartphone app that lets you book a cab. Uber heavily relies on Big Data. After all, Uber must maintain a large database of drivers, customers, and other information. As a result, it is rooted in Big Data and uses it to derive insights and provide the best services to its users. The big data principle is shared by Uber and crowdsourcing. In other words, registered drivers in the area can assist anyone who needs to get somewhere.
It computes the time taken using various algorithms that also take into account traffic density and weather conditions. To calculate its surge pricing, Uber makes the best use of data science. When there are fewer drivers available to serve more riders, the cost of the ride rises. This only happens when there aren’t enough drivers in a given area.
However, if there is less demand for Uber rides, Uber charges a lower rate. This dynamic pricing model is based on Big Data and makes excellent use of data science to calculate fares based on parameters.
In the next article, I am going to discuss the Life Cycle of a Data Science Project in detail. Here, in this article, I try to explain Why Data Scientists are in Demand and I hope you enjoy this Why Data Scientists are in Demand article.