Data scientists are critical in helping companies develop effective business strategies. Data science is about mining big data and identifying hidden patterns to draw meaningful insights for business leaders. Data is a significant corporate asset, and by better understanding the data, companies can make more insightful business decisions. Here is our list of the top 10 data science skills companies want to see in 2022.
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1. Machine Learning
Machine learning is a subset of Al that analyzes large chunks of data to self-learn, grow and change as scientists feed it new data. These outputs are valuable in providing insight leaders need to perform high-value predictions and make informed business decisions.
Machine learning is an integral part of data science. Data scientists use statistical methods to train algorithms to make classifications or predictions, revealing key insights in analysis. These insights then drive business and application decisions, influencing growth metrics. As big data grows, the market demand for scientists will help identify what companies need to answer the questions that drive business strategy.
Here are a couple of common ML use cases:
- Healthcare: Machine learning (ML) is revolutionizing the healthcare ML is powering robotic surgeries, telemedicine chatbots, and medical imaging and diagnosis. Researchers at MIT developed an Al algorithm to predict breast cancer. The team trained their Al model using 20,000 prior patient exams.
- Online Fraud Detection: Financial institution Capital One uses machine learning to detect, diagnose, and remediate anomalous app behavior in real-time. It also uses the technology as part of its anti-money laundering tactics to adapt quickly to changes in criminals’ behaviors.
Of all the programming languages, many find that Pytthing is the easiest to learn with the shortest learning curve. .. Its simple syntax and improved readability make it ideal for new scientists. Python also includes a plethora of information mining tools and provides robust capabilities for machine learning.
Data analytics is an essential component of data science. Analytics tools provide information about various matrices required to evaluate business performance. Python is a better programming language to use when creating data analytics tools.
Python can easily provide improved insight, recognize patterns, and correlate information from large datasets. Python is also useful for self-service analytics.
Python Is Essential for Deep Learning
Tensorflow, Keras, and Theano are just a few Python packages that help scientists develop deep learning algorithms. When it comes to deep learning algorithms, Python provides better support. Deep learning algorithms are based on neural networks found in the human brain. It is concerned with the development of artificial neural networks that mimic the behavior of the human brain. Deep learning neural networks apply weighting and biasing to various input parameters to produce the desired output.
3. R Analytics
R analytics (also known as the R programming language) is an open-source software used for data science, statistics, and visualization projects. It is a powerful, versatile programming language that easily integrates business intelligence (Bl) tools to help you make the most of business-critical information.
R is popular among statisticians because it generates visualizations, including graphs, charts, pictures, and various plots. Bl analysts can use these visualizations to identify trends, outliers and patterns in data.
Another reason for its popularity is that its command-line scripting allows users to save complex analytical methods in steps, which can then be reused with new models later. A few additional benefits of R include:
Providing More In-Depth, Accurate Insights
Risa powerful tool for modeling and analyzing large datasets. Data scientists can deliver more valuable insights to users with more precise details through R analytics.
Leveraging Big Data Analytics
R helps with big data querying, and many industry leaders use it to leverage big data across their businesses. Scientists can discover new insights and use R analytics to make sense of their information. R can handle these large datasets and is as easy to use.
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Hadoop is a big data distributed processing platform that handles processing and storage in scalable clusters of computer servers. Hadoop is crucial to a big data ecosystem. It supports advanced analytics initiatives such as predictive analytics, mining, and machine learning across large datasets. Hadoop systems handle both structured and unstructured information. This gives scientists more flexibility in data gathering, processing, analysis, and management than relational databases and data warehouses.
Interactive querying, stream processing, and real-time analytics are just a few of the new applications that YARN has added to Hadoop clusters. Manufacturers, utilities, oil and gas companies, and other businesses need real-time analysis from the Internet of Things (loT) devices. These companies stream information into Hadoop systems to try to detect equipment failures before they happen.
5. Cloud Computing
The cloud is a collection of computers capable of storing information and performing complex calculations. The phrase “in the cloud” means data is accessible over the internet.
The cloud appeals to scientists for a variety of reasons:
- Processor Power: Training Al and ML algorithms require a lot of processing power. The amount of power required could easily overwhelm a local computer. However, a data center with powerful GPUs (graphic processing units) dedicated to these calculations can handle these tasks easily.
- Data Aggregation to Use in Analysis: One of the most difficult tasks for a data scientist is aggregating information. When the information is stored in the cloud, any computer in any location can access it. This is especially helpful when aggregating from a variety of sources.
- Big Data: Scientists need a lot of hardware to store big data. Downloading it all to a local computer to run a calculation isn’t an option. Working in the cloud eliminates this problem.
- Share Results: Storing analyses in the cloud makes it simple for others to contribute or replicate the results. Data scientists make the results available as a dashboard or webpage for business leaders to access.
6. Unstructured Data Management
The emphasis on structured information makes sense from a practical standpoint. Structured information is formatted consistently (e.g., by predetermined fields), making it simple for scientists to search through, sort, and compare against one another.
Structured information is well-organized, factual, and concise. It usually takes the shape of characters and numbers that fit neatly into table rows and columns. Structured data is typically seen in tables, which are similar to Excel spreadsheets and Google Sheets.
So, what’s the problem with data scientists focusing on structured information? It’s because it only represents a small portion of a company’s valuable assets. Unstructured data does not come from a searchable database. It comes from sources such as emails, phone calls, presentations, movies, and various other sources.
Unstructured information does not have a predefined structure and can take many different forms, such as picture and text files to video and audio files, to mention a few.
7. Data Visualization
Data visualization presents data analytics in an easy-to-understand format. Data scientists summarize complex concepts into reports. Even those who aren’t familiar with information analysis can understand it. Data visualization can also be used to:
Managers need a complete picture before they can make accurate decisions. However, because there are so many analyses and results, the easiest and quickest way to provide them is to use visualization techniques.
Present Information to Stakeholders
Stakeholders don’t care how scientists approach the model. All they care about is getting the solution they’re looking for. Presenting analysis in a format that stakeholders understand helps them determine whether it meets their needs. It also allows them to communicate feedback about the analysis.
8. Predictive Analytics and Modeling
Organizations across industries have come to rely on the massive amount of information humans generate. Data scientists can now gain a level of insight beyond a description of past behavior and instead look ahead at future possibilities by
using predictive analytics. This analysis is important because it helps them better understand their customers and identify behavioral patterns. Consider the following example from Northeastern University:
When a doctor wants to predict a new patient’s cholesterol-based solely on their BMI, a linear regression model can help. In this case, the analyst would know to enter the information the doctor gathered from his 5,000 other patients into the linear regression model, including their BMls and cholesterol levels. They’re attempting to forecast an unknown based on a set of quantifiable information.
The linear regression model would take the data, plot it on a graph, and draw a line through the middle that accurately depicted the shortest distance between all plotted information points. When a new patient arrives with only their BMIof 31, a data scientist will predict the patient’s cholesterol by looking at that line and seeing which cholesterol level most closely resembles that of other patients with a BMI of 31.
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9. Fundamental Statistics
Statistics help to describe the information in quantitative measures. Rather than sifting through a large amount of data, scientists can use a few statistical calculations to make sense of it.
Statistics not only assist us in comprehending what we have but can help infer meaningful results based on a small set of information. Inference is important because scientists sometimes don’t have a full dataset to work with.
Consider the following scenario: You work for a chain store and are tasked with analyzing and comparing the sales patterns of stores in two different countries. The entire scope would be the sales information collected over the course of the stores· existence. Analyzing the entire dataset is inefficient. Instead, you take samples from both groups. You can compare the stores using the sample data set. If the sample results apply to the entire scope, we can use inferential statistics. You take samples from both groups instead. You can compare the stores and analyze the sample data. We can use inferential statistics if the sample results apply to the entire scope.
Inferential statistics terms and concepts include hypothesis testing, p-value, statistical significance, and z-score. A data scientist should apply these concepts and have a thorough understanding of them.
Scientists can draw conclusions about a population using inferential statistics based on findings from a small set of information. It’s crucial because we’ll most likely be working with sample information rather than the entire population.
10. Social Media Mining
Social media mining is when an organization gathers and analyzes data from social media users to form conclusions about the users. The datasets are utilized to target certain market segments.
Recognize Trends as They Emerge
Thanks to techniques like social listening, social media data mining can spot trends before they become trends. Data scientists can look at which topics, keywords, and mentions are trending and use mining techniques to figure out why.
Keep an Eye on What’s Going On in Real-Time
Another compelling reason to employ the technique is to detect events. Researchers and government agencies can use heat mapping or similar techniques to map major disruptions as they occur by mining social media mentions. Traditional sensor methods can’t keep up with emerging situations or understand the context as quickly as they can with these devices.
Because many users post using their phones, event detection is in real-time and up to the minute. Organizations can respond more quickly when users share information during disasters or civil and social movements.
Provide Relevant Content
Organizations can use social media mining to personalize customer interaction and reduce spam. This idea can save people a lot of time and frustration while they are online. Companies can use social media heat mapping to identify micro-trends and communicate with potential customers.