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edujournalblogs · 2 years ago

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The Future of Data Science: Trends to Watch and Skills to Develop

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, ecommerce, 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 Analyst / Data Scientists to analyze the data to optimize its profits.

There are five stages in data science life cycle:

1) Data Extraction: Extracting data from 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 : Developing a reasonable scientific argument to comprehend your data and provide your inferences as conclusion.

Trends to Watch 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 support numerous libraries that helps in Data Analysis such as numpy, pandas, matplotlib, TensorFlow, keras, 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 solution 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 huge volume of data present), and imagine what would happen if the businesses do not have control over their data. Hence, most of the companies are adopting AI and machine learning models. For this reason, we have Dataiku, that provides end-to-end data science solution 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 a 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 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 machine learning algorithm. Hence, the output derived is always reliable and accurate. They can provide insight in a way that non-technical stake holders can easily comprehend and understand.

4. Data Privacy: With 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 market places for insights and analytics is 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 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 is a valuable tool for Data Analytics. Also, they streamline the massive datasets that drive AL and ML operations. A multi-cloud architecture is indispensible for a business looking to develop in data science capabilities.

7. Big Data : The sheer amount of data present in business houses, and the tools used to analyze the information has expanded exponentially. The problem lies with 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 that can help businesses make better decisions.

8. Data Visualization : Data Visualization is the process of displaying information in graphical form. Data visualization tools enables 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.

Skills to Develop :

Core Skills :

Cloud technologies, Machine Learning, Mathematics, Statistical analysis technique, 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

Soft Skills:

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

Conclusion :

As we move forward into your future, we hope to see more innovations and technological advancement down the line in the areas of Data Analytics, Data Science, Artificial Intelligence, Machine Learning, Data Management, Cloud computing etc. Also, these advancements reflect in the various domains such as healthcare, ecommerce, 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 will be the returns for the company.

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#data_science #data_visualization #communication_skills #Predictive_Analysis #NLP #Artificial_Intelligence #Machine_Learning #Python #Cloud_Technologies #Data_Intepretation

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