Data Science is the new powerhouse driving the industry to greater heights by processing Big Data with Machine Learning methods.

  1. This article provides a comprehensive overview of the processes involved in Data Science.
  2. It also explains the importance of different tools and recent advancements that have lowered the barrier by computing complex problems.

Introduction to Data Science

  • Data Science is a multidisciplinary field combining Statistics, Programming, and domain expertise to derive applications for solving business problems.
  • The term Data Science has emerged with the evolution of Big Data, Computation Power, and Statistics.
  • The core job of Data Scientists is to manifest solutions to create a positive impact by providing practical solutions.
  • As organizations attempt to become Data-driven, Data Science endeavours the key to success in the competitive market. 

Understanding the Lifecycle of Data Science

  • Defining Business Problem
  • Data Collection and Preparation
  • Exploratory Data Analysis
  • Model Building
  • Model Optimization
  • Model Deployment and Evaluation

6 Key Tools used in Data Science

1. Statistics and Probability 

  1. Data Science is built on the foundations of Statistics and Probability. Probability Theory is quite useful in formulating predictions.
  2. Data Science relies heavily on Estimations and Projections.
  3. We make Estimations for further examination with the use of Statistical approaches. As a result, Statistical approaches are heavily reliant on Probability Theory.
  4. All the Statistics and Probability is based on Data.

2. Data Visualization Skills

  1. People skim and skip the most essential stories in the newspaper, but the ones that people read are mostly Sketches.
  2. It is a human concept to see something and have it registered in one’s mind.
  3. The complete Dataset, which may number in the hundreds of pages, can be reduced to two or three Graphs or Plots. To create Graphs, one must first visualize the Data Patterns.
  4. Microsoft Excel is a fantastic program that generates the appropriate Charts and Graphs based on our requirements.
  5. Tableau, Metabase, and Power BI are some examples of other Data Visualisation and Business Intelligence tools.

3. SQL

  1. SQL (Structured Query Language) is a standard Programming Language used to fetch Data to perform Exploratory Data Analysis, making it useful in insights generation and Model Development.
  2. In any organization, Data Collection and Analysis are performed by querying vast Databases with SQL.

4. Big Data

  1. Processing Big Data is complex and may require plenty of time with traditional tools.
  2. As collected Data are primarily Unstructured, tools such as Apache Hive are frameworks integrating SQL on top of Hadoop to allow working with Unstructured Information.
  3. It allows users to read, write, and efficiently process petabytes of Data.

5. Programming Language

  1. Data Analysis and Model Development are done using Programming Languages like Python, R, and various other tools.
  2. It helps in carrying out Statistical Tests and advanced transformations to obtain quality Data. Eventually, the Data is used to build Models to automate strenuous business operations.
  3. One of the most widely used Programming Languages in Data Science is Python, it provides several packages and libraries for building Machine Learning or Deep Learning Models with ease.
  4. However, the R Programming Language is better for Statistical Analysis and Data Exploration.
  5. Different Programming Languages are subtle to perform different tasks and should be embraced accordingly to the needs.

6. Communication

  1. Data Scientists are likely to present their findings to non-technical audiences or Business Executives.
  2. The most important thing is to understand the viewers and speak in their language.
  3. To ensure there are no communication gaps, Data Scientists should focus on values and outcomes while aligning them with the interest of stakeholders.

Advancements in Data Science

The following are a few recent advancements in Data Science:

  • AutoML: Automation of Machine Learning tasks is one of the most challenging initiatives in Data Science. However, companies like DataRobot and H2O.ai have established themselves by providing state-of-the-art AutoML solutions to help organizations perform end-to-end Machine Learning processes.
  • Data Privacy: Privacy and Security of Data are always sensitive, any hindrance may result in losing trust in future business deals. The entire Data Science process is fueled by Data, hence it becomes a priority to eliminate Data leakage by implementing privacy-preserving techniques like Federated Machine Learning.  
  • MLOps: Among many aspects of Machine Learning, MLOPs has positively impacted organizations by adding value to AI-based Project Management. It assists in the Scalability, Tracking, and Auditing of Models during Development. MLOps not only mitigate risks of inaccurate predictions by optimizing Models but also automate Machine Learning Pipelines to accelerate the Project life cycle.
  • NLP: Advancements in Deep Learning have enhanced the capabilities of integrating Natural Language Processing (NLP) into Data Science. Huge Datasets of text or audio can be transformed into either numerical Data or broken down into tokens for Standard Analysis. T

Challenges in Data Science Technologies

  • Most Data Science Projects fail to positively impact business processes due to poor Data quality.
  • Data Scientists lack the skills to explain the technical jargon and complexities of a Model, leading to the loss of stakeholder’s interest.
  • Organizations lack security checks to safeguard their Data and Models, making them vulnerable to Cyber Attacks and Adversarial Attacks, respectively.
  • Many companies have a misconception of this role and expect Data Scientists to be the jack of all Data processes.

Conclusion

  • Along with the tools used to perform Model Analysis and Development, this article talks about the entire lifecycle of the Data Science Project.
  • It also emphasizes the impact of advancements in Data Science and the current challenges in this field.
  • The first step in implementing any Data Science Algorithm is integrating the Data from all sources. However, most businesses today have an extremely high volume of Data with a dynamic structure that is stored across numerous applications.
  • Creating a Data Pipeline from scratch for such Data is a complex process since businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased Data Volume and Schema Variations.
  • Businesses can instead use automated platforms like Hevo.
Amit Kulkarni
Technical Content Writer, Hevo Data

Amit Kulkarni specializes in creating informative and engaging content on data science, leveraging his problem-solving and analytical thinking skills. He excels in delivering AI and automation solutions, developing generative chatbots, and providing data-driven AI & ML solutions. Amit holds a Master's degree and a Bachelor's degree in Electrical Engineering, consistently achieving distinction in his studies.

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