Data Science is one of the most lucrative career options today. The role of a data Scientist is to assist the management in making business decisions. The future of Data Science is expected to bring opportunities in various areas of healthcare, e-commerce, automobile, cyber security, banking, finance, scientific research and education, insurance, entertainment, telecommunication, etc.
Business organizations have adopted data-driven models to simplify their processes and make business decisions based on the insights derived from data analytics. Every organization wants to make super profits in a short span of time. As data is the key factor in Data Science, every industry realizes that it requires Data analysts / Data Scientists to analyze the data to optimize its profits.
There are five stages in the data science life cycle:
1) Data Extraction: Extracting data from the source for subsequent processing and analysis.
2) Scrubbing Data: Here the data is cleansed, and all the redundant and irrelevant data will be eliminated.
3) Data Exploration: Exploring and visualizing data in order to discover insights or trends.
4) Model Building: Selecting a statistical, mathematical, or simulating model to acquire insights and make predictions.
5) Data Interpretations: Develop a reasonable scientific argument to comprehend your data and provide your inferences as a conclusion.
Trends in data science
1. The growth of Python Language over the years: Python is on track to become the most popular programming language in the years to come. Python supports numerous libraries that help in Data Analysis such as numpy, pandas, matplotlib, TensorFlow, Keras, and Scikit-learn, etc., They are the go-to language in the field of data science.
2. Growing demand for end-to-end Artificial Intelligence (AI) Solutions: Poor quality data can lead to inaccurate results. Hence, we need to clean large data sets and build machine learning models, thus, gaining valuable and deep learning insights from the massive quantity of data.
Businesses that provide end-to-end data science solutions from start to finish within a single product will dominate the market. The growing problem with many businesses is that most of the businesses could not analyze or categorize all the data that was stored (ie., studies show that almost 35% of stored data remain unutilized due to the huge volume of data present), and imagine what would happen if the businesses do not have control over their data. Hence, most of companies are adopting fAI and machine learning models. For this reason, we have Dataiku, which provides end-to-end data science solutions from start to finish, which is what companies want. Hence, AI-specific skillsets are becoming increasingly prominent across all sectors. Also, we have a concept of scalable AI, which refers to algorithms, data models, and infrastructure capable of operating at the speed, size, and complexity required for the task. Scalability contributes to solving scarcity and collection issues of quality data. The development of ML and AI for scalability requires the setting up of data pipelines.
3. Huge Demand for Data Analyst and Data Scientists: As the demand for data grows, there is a corresponding demand for experts to parse and analyze data and gain insights.
Yes, it is true that automation can replace the tasks done by humans previously, but, actually, the data found in Big Data are massive and are often messy and unstructured, which is why humans are required to manually clean and reprocess the data before it is ingested by the machine learning algorithm. Hence, the output derived is always reliable and accurate. They can provide insight in a way that non-technical stakeholders can easily comprehend and understand.
4. Data Privacy: With the increasing amount of data being consumed on a daily basis, there is a growing concern about data breaches and privacy violations.
You will need to have control over who sees what you share. Data exchanges in marketplaces for insights and analytics are one of the prominent trends which is also known as Data as a Service (DaaS). The data can be used by businesses as part of the business process.
5. Automation: Automation can play a big role in data cleansing, data integration, data management analytics, and resolving any data legacy issues and hence can speed up the process. Automation (robots, chatbots, virtual assistants, etc.) is likely to escalate the employability of skilled individuals.
6. Cloud Technologies: Cloud Technologies optimize the value of enterprise data. They enable businesses to store and access data from the cloud, eliminating the need for in-house servers. Cloud-based data analytics tools are easily accessible and are a valuable tool for Data Analytics. Also, they streamline the massive datasets that drive AL and ML operations. A multi-cloud architecture is indispensable for a business looking to develop data science capabilities.
7. Big Data: The sheer amount of data in business houses, and the tools used to analyze the information has expanded exponentially. The problem lies with the collection, cleaning, structuring, formatting, analyzing, etc. Data science models and AI can help solve these issues and storage issues can be handled by storing in the cloud. Big data provides a wealth of insights to help businesses make better decisions.
8. Data Visualization: Data Visualization is the process of displaying information in graphical form. Data visualization tools enable us to see patterns, trends, outliers in data, etc by using visual elements like graphs, charts, maps, etc., it makes complex data easier to understand and interpret. Data visualization tools are also becoming more sophisticated, allowing data analytics professionals to create highly engaging and interactive visualizations.
9. Natural Language Processing: NLP enables computers to understand and interpret human language which is an important trend in Data Science. NLP can also be used to develop robots, chatbots, and virtual assistants that can interact with customers in natural language.
10. Predictive Analysis: Makes use of historical data and past trends and projections to make future predictions and projections.
Some Data Science Job Trends :
- Healthcare: Helps doctors understand the pattern of disease, provide an accurate diagnosis, keep track of patient’s health, and informed decision-making. Also, an AI-based medical assessment platform helps to analyze existing records to categorize patients based on the risk of experiencing a particular illness and that can predict the success rate of different treatment plans.
2. Cyber Security: Detect online fraudulent activities to prevent losses.
3 Genomics: Study DNA Sequencing, structure, mapping, etc which can lead to the groundbreaking advancement of medical science which could lead to the creation of more jobs.
4. Automobile: We will be dealing with driverless vehicles in the future for which we require skilled Data Scientists.
5. E-commerce: For improving customer service and providing better user experience we require the data. The software could be more user-friendly, provide personalized service, reduce glitches during operations, etc.
6. Banking: A bank providing faster loan service using machine learning-powered credit risk models and a hybrid cloud computing architecture that is both powerful and secure.
7. Media: The digital media company employs deep analytics and machine learning to gather real-time insights into viewers’ behaviors.
8. Logistics: AI Systems such as Google Maps can help in navigation.
The boom in data science jobs is causing businesses to rethink their approach to specific data science roles. For instance, Companies want sector specific-skills, that require specialized skills, such as AI, and then think about machine learning or parallel computing, which will determine where you want to go and how your career develops. Keeping track of these trends will help you surge ahead of others.
Core Skills :
Cloud technologies, Machine Learning, Mathematics, Statistical analysis techniques, data science, Data Analytics, Python, Artificial Intelligence, database management, Big Data,, SQL and Data Warehousing, analytical tools such as Hadoop and Spark, Power BI, Tableau, SAS (Statistical Analytics Software), etc
Some common Python libraries used in Data Science for data analysis:
Pandas, numpy, matplotlib , TensorFlow , Scipy, keras, scikit-learn
1. Communication Skills (verbal, written & presentation skills) connecting the business side with the technical and scientific side for sharing insights in a manner that they can quickly comprehend and understand and make an inference.
2. Business Acumen: Data scientists must understand the context and goals of the business to generate meaningful insights. They must work closely with business stakeholders to understand their needs and objectives. By collaborating with business leaders, data scientists can gain a better understanding of the business context and generate insights that are relevant and actionable.
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As we move forward into your future, we hope to see more innovations and technological advancement down the line in the area of Data Analytics, Data Science, Artificial Intelligence, Machine Learning, Data Management, Cloud computing, etc. Also, these advancements reflect in various domains such as healthcare, e-commerce, banking, cyber security, scientific research and education, and more., And the best way to prepare yourself for the coming changes in data science is to upskill/reskill yourself and continue learning. The more robust a data team is, the greater the returns for the company will be.
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