Data science is an interdisciplinary field based on scientific methods, processes, and algorithms for extracting meaningful insights from large amounts of data. Although the field traces back to 1900’s, it is only recently that it has taken off. There’s about a wealth of meaningful insights stored in the database and data lakes. These data can be used to transform the current industry by developing more innovative products and services. However, due to a lack of interpretation, these data are largely unexplored. This is where data science comes into play. Data science identifies trends and derives valuable insights, allowing businesses all over the world to act on data in an efficient and productive way.

Data science is a subset of AI (artificial intelligence) that allows data scientists to extract valuable insights from large amounts of data using statistics, analysis, and other scientific methods, whereas machine learning is a subset of AI that uses techniques to deliver AL applications. Deep learning is a subset of machine learning that solves more complex problems. Among others, predictive analytics is a subset of data science that provides insight into future trends based on past data or data crossover between multiple data lakes and sources. These terms are related to the field of data science and have an interdependence pattern. Although they are used interchangeably, it is critical to distinguish their areas of expertise and functions in data science.

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Data science encourages the use of machine learning to train models to learn from available data rather than relying on business analysts to discover insights from the data. At the juncture of technological evolution, the importance of data science is felt in every industry around the world. In the present and near future, data science as a dynamic field will have a broader scope. Data science offers diverse career path.

Data Science Fundamentals

  • Statistics: Applied statistics facilitates data interpretation.
  • Programming: Programming languages such as R and Python are essential for automating data manipulation.
  • Data modeling: Data modeling is the process of formatting specific data into a database.
  • Data visualization: Data visualization is the use of graphical representations of data to reveal trends and insights.
  • Machine learning: Machine learning is essentially a collection of techniques used to predict and forecast data.
  • Big Data: Apache Hadoop and Apache Spark are open-source distributed systems that allow data scientists to manage large amounts of data.
  • Teamwork: Data science projects frequently involve a group of data engineers, software developers, business analysts, and so on.

Data Science Lifecycle

Data science is usually an iterative process of analyzing and acting upon data. To maximize the benefits of each process phase, it is critical to understand the data science lifecycle. For data modeling projects, the data science lifecycle follows the following pattern.

  • Planning: entails determining the scope of a project and its potential outcomes.
  • Creating a data model: Data scientists are expected to use the appropriate tools and data to create machine learning models. As a result, they frequently employ a variety of open-source libraries and in-data tools.
  • Evaluating the data model: Model evaluation is a necessary step in model deployment. It generates evaluation metrics and visualizations to assess the model’s performance in relation to new data. Only when the model achieves a high percentage of accuracy can it be deployed.
  • Explanation of a model: It has become increasingly important for data scientists to curate an explanation of a model.
  • Explaining a model: It is becoming increasingly important for data scientists to curate an automated explanation on significant factors such as generating predictions and model-specific explanatory details on model predictions. This necessitates excellent communication skills in order to explain technical results to non-technical colleagues.
  • Deploying a model: By operationalizing models as scalable and secure APIs or utilizing in-database model learning machines, it has made the process of getting the machine learning model into the right system more convenient.
  • Monitoring a model: It is considered imperative to monitor the model’s performance against the data to ensure consistency in the results.

What is Data Scientist?

The term data scientist was coined in 2008, in response to the need for skilled professionals to analyze a wide range of data. They extract valuable insights from customers, smartphones, the web, and other sources to uncover the pattern and provide informed insights to the firm. As a data scientist professional, you can attract a pool of opportunities that will help you advance your career. Data science enables a company to develop technical acumen by effectively collecting relevant data from a data reservoir, applying critical analyses, and interpreting the results into solutions.

Numerous organizations recognize the combined skills acquired by talented data scientists as they witness the proven benefits of data science applications. Data science is typically carried out by a team that includes a business analyst, data engineer, IT architect, and application developer in addition to a data scientist. According to Glassdoor, a data scientist is one of the top three jobs in America. However, this rising demand is being hampered by a scarcity of skilled data scientists. Top companies view data scientists as a valuable asset to their organization.

Data Science Tools

Building, evaluating, and deploying a machine learning model can be a time-consuming process. As a result, data scientists use a variety of data science tools to accelerate these processes. It is critical to understand the tools that are used in data science work.

Tableau

Tableau is a data science visualization software that creates powerful interactive graphs for geographical data visualization by plotting longitudes and latitudes on a map. Moreover, it can communicate with databases, spreadsheets, and OLAP (Online Analytical Processing), among other things. Along with visualization, tableau’s data analysis tool can be used to analyze data.

Spark (Apache)

Apache Spark is an analytical engine that is primarily intended for batch and stream processing. Its numerous APIs enable data scientists to make valuable predictions based on the extracted data. Apache Spark outperforms Hadoop and is significantly faster than MapReduce. Many businesses rely on Apache Spark to process large databases.

TensorFlow

TensorFlow is a well-known machine learning tool that is widely used by data scientists for advanced machine learning algorithms. It takes its name from tensors, which are multidimensional arrays. Tensor has a wide range of applications, including speech recognition, drug discovery, image classification, and image and language generation. Its open-source, dynamic toolkit enables high performance and computational capabilities.

Jupyter

Jupyter is a completely open-source tool that provides an interactive computing environment in which data scientists can perform all of their tasks. It supports a variety of programming languages, including R, Python, and Julia. Jupyter Notebooks is a popular data science notebook that helps with data cleaning, visualization, machine learning model creation, running code, and presentations. In data science, RStudio, Zeppelin, and Jupyter are commonly used to conduct analysis.

MATLAB

MATLAB is proprietary software. It is primarily used by data scientists to stimulate neural networks. MATLAB is used in a variety of scientific disciplines to help with matrix functions, statistical data modeling, algorithmic implementations, creating powerful graphs for visualization, and image and signal processing.

BigML

BigML is a cloud-based data science tool that is used to process Machine Learning Algorithms and to facilitate predictive modeling. It makes use of a variety of machine learning algorithms such as clustering, classification, time-series forecasting, and so on. BigML offers interactive data visualizations as well as various automation methods for automating hyperparameter model tuning.

SAS

SAS is proprietary closed-source software that is specifically designed for statistical operations. It is used to analyze data in many large organizations. Besides, is a highly dependable software that provides a plethora of statistical libraries and tools that allow data scientists to build models and organize data.

There are several other tools that cater to data science domain applications. Most tools provide complex operations in a single location, making it easier for data professionals to perform the functions without having to write the codes from scratch.

DataRobot

DataRobot is the most valuable data science tool used for operations integrated with Machine learning, Augmented Analytics, Statistical Modelling, and Artificial Intelligence. Moreover, it offers beginners and expert friendly GUI that makes data analytics possible. It facilitates in providing high-end automation to the customers, making intelligent data-based decision and deriving rich insights. About 100 data science models can be deployed via DataRobot.

Data Science Career Path

Unlike other traditional jobs, a data science career path does not necessitate a technical bachelor’s or master’s degree. All that is needed are effective skills and experience. Furthermore, it is critical to understand the intricacies of data science skills and career paths. Fundamental skills and knowledge in data management, data visualization, data mining, data analytics, and programming are expected of data science professionals. Data science career paths are largely flexible and adaptive. The skills required for data science can be gained through experience or independently through a data science course. For example, a data analyst can advance to the level of data scientist by learning more about data management, artificial intelligence, statistics, and big data analytics.

The expertise in the field determines the transition of roles. There are numerous data science domains and career paths that necessitate specific skills and qualifications. Aspiring professionals can specialize in a variety of fields, including medical science, consulting, business, and retail. In addition to traditional job roles, candidates with an interest in marketing, finance, accounting, operations, supply chain, and specialized departmental analytics can hone their skills. Data science is one of the most lucrative jobs available today.

The term ‘data scientist’ is used interchangeably for roles in data science. However, the functions performed by data science professionals are distinct based on qualification and on-the-job requirements in the field. Data science is a discipline that emphasizes statistics and algorithms, as well as communication and management skills. These abilities are equally important for effective teamwork.

Data Analyst

A data analyst, also known as a business analyst or analytics professional, is someone who has technical knowledge of how to generate actionable insights from company data. The projects that are being worked on change from time to time. The following designation of a data analyst may be flexible depending on the skills and experience in the field.

  • Data analyst
  • Associate data analyst
  • Senior data analyst
  • Product manager
  • Lead data analyst
  • Director

Skills required: Python, R programming, data modeling, Tableau, business acumen, database cleaning, effective presentation, visualization, etc.

Data Engineer

A data engineer’s primary responsibility is to build a software infrastructure that ensures correct data is delivered to the appropriate department. Synonymous roles such as data architect and quantitative analyst necessitate extensive organizing and programming abilities. Because the role is more specialized and central to the organization, lateral movement is less visible in the following designation.

  • Junior data engineer
  • Associate data engineer
  • Senior data engineer
  • Product manager
  • Lead data engineer
  • VP/ SVP

Skills required: database management, data cleaning, Python or R programming, Hadoop, business acumen, effective presentation, visualization/BI

Business Intelligence Developer

A business intelligence developer or system analyst is skilled in analytics as well as the IT department. A business intelligence developer’s role overlaps with key functions such as data science, programming, and data architecture, to name a few. As the emphasis is on technical details rather than analytics, expertise in machine learning techniques is required. Since the role now has authority over other departments, it suggests the possibility of a lateral move up the corporate ladder.

  • BI Developer
  • Senior BI
  • Product Manager
  • Team Lead
  • Director

Skills required: Python or R programming, Hadoop, creating models, notebooks, Github, data modelling, business acumen and visualization/BI.

Data Scientist

Data scientist is the most sought-after role in an organization. In today’s work environment, data scientists are responsible for tasks such as developing machine learning models for prediction, driving insights, uncovering patterns, visualizing data, and pitching marketing strategies. The corporate ladder for a data scientist, synonymously known as a market analyst or BI expert follows the given pattern.

  • Junior data scientist
  • Associate data scientist
  • Senior data scientist
  • Product manager
  • Lead data scientist
  • Director

Skills required: statistics, mathematics, data modeling, Python and R programming, database skills, business acumen, presentation, visualization/ BI.

Statistician

As the title implies a statistician has expertise in statistical theories and data organization. Along with extraction of valuable insights from massive datasets, statisticians create new methodologies for application. The responsibility of a statisticians in data science mainly include collecting, analysing and interpreting data along with coordinating with cross-functional teams, communicating findings to stakeholders, consulting on business strategies, etc. based on the data.

  • Junior Statistician
  • Associate Statistician
  • Senior Statistician
  • Director

Skills required: statistics, mathematics, data mining, machine learning, SQL.

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At Henry Harvin, you can avail a comprehensive data science course package. It is one of the top institutes that offer data science courses to aspiring candidates. The course structure is intended to provide you with real-world data science skills and knowledge. The course curriculum adheres to best industrial practices. It takes a practical and simple approach to training. Henry Harvin effectively blends theory, computation, and application for a better understanding of the core subject.

The knowledgeable instructors provide cutting-edge data science training and guidance throughout the course. Along the way, Henry Harvin provides summer internship programs to assist candidates in getting started in the field of analytics and data science and gaining industrial experience.

Over 1000 satisfied students from all over the world have benefited from Henry Harvin’s course.

  • Courses in Data Science
  • Python for Data Science Course
  • R Programming for Data Science Course
  • Post Graduate Program in Data Science
  • Data Science with R Course
  • Statistics for Data Science Course
  • Data Science with Python Course

Why Data Science?

Data science’s impact extends beyond business goals to health care. Many industries use data science techniques and tools to reduce costs, increase revenue, and improve customer satisfaction. These advantages are derived from a data set in order to extract valuable insights, discover unseen patterns, and make critical business decisions. Almost every industry is currently experiencing a surge in data generation. These data can be used to help orchestrate business strategies. As a result, it is critical for businesses to hire data science professionals to help them achieve their objectives.

Several job opportunities

As an in-demand field, data science offers numerous opportunities. It has given rise to a wide range of professions, including data scientist, business analyst, data analyst, research analyst, analytics manager, and big data engineer.

Hiring benefits

It has become comparatively easier for the organization to recruit talented data, science professionals. Processes such as big data and data mining have made it easier for the recruiting team to choose CVs, aptitude tests, games, and other resources.

High salary

Data science is one of the highest-paying jobs. As many organizations seek a skilled data science professional, talented candidates are compensated well for their services. Data science professionals can move up the corporate ladder with the right skills and experience.

Profitability in business

Data science enables businesses to increase profits by facilitating product creation and launching to the right audience at the right time. Data insights enable quick business strategies and action, resulting in revenue growth. It improves business intelligence, business prediction, complex data interpretation, marketing and sales growth, and information security. Process automation aids in the recruitment of skilled data science professionals.

Data science allows organizations to define their goals and target audiences in order to provide a better customer experience and maximize profits. Data science professionals use data-driven evidence to make business decisions.

Conclusion

The field of data science is estimated to expand in the future. Every organization recognizes its significance and versatility. Hence, many top companies have handsomely invested in data science. Nearly every organization has adopted data science to improve production, services, business acumen, and customer satisfaction, among other things. Using technology to boost the economy has become increasingly important. Data science takes an interesting approach as it has an infinite number of skills, advancements, and discoveries in research, among other benefits. It goes without saying, that data science career path is an excellent choice for talented individuals.

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FAQ’s

Q1. Is Data Science difficult to learn?

Ans.  Because data science employs unique techniques and functions in comparison to other fields, it may appear difficult. However, with the right course and guidance, aspiring candidates can learn the necessary skillsets for data science.

Q2. How much does a data scientist make on average?

Ans. According to the study, the average salary of a data scientist may start at Rs.8,50,000 and may increase based on skills and experience in the field.

Q3. What is data cleansing?

Ans. Data cleansing is the process of cleaning data that has been compiled in one area. It ensures that incomplete information, incorrect format, and irrelevant data are updated and corrected within the database. Moreover, data cleansing boosts an organization’s data quality and overall productivity

Q4. Why is Python popular in data science?

Ans. Python is a programming language that provides a high-performance data analysis tool, an easy-to-use data structure, and learning flexibility. Python has a wide range of applications and is used in a variety of industries.

Q5. Is it difficult to land a job in data science?

Ans. Since, data science is a newly emerged field and requires specific skillsets, it is important to update your skills to match the need of evolving tools and techniques employed in data science. You might be able to land a job in data science with the right skills and experience.

Q6. How to I get started in data science?

Ans. Knowledge and skills in applied statistics, data modeling, data management, warehousing, and deep learning are required to begin a career in data science. You can begin by looking into the data science courses offered by the Henry Harvin Institute.

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4 Comments

  1. Vidya Pradhan Reply

    The article on “Data Science Career Path” in Henry Harvin provides an in-depth analysis of the different roles available in the field of data science. It discusses the skills required for each role, the responsibilities, and the job prospects.

  2. Vipul Warsi Reply

    The article is an excellent resource for anyone who is interested in pursuing a career in data science. It provides a comprehensive overview of the field and offers practical advice on how to build a successful career in this exciting and rapidly growing industry.

  3. Hardik Bhagela Reply

    Completing a course in data science provides valuable skills and knowledge that can be applied to a variety of industries and job positions. Overall, data science course is a valuable investment in the career.

  4. This article on the data science career path provides a comprehensive guide for individuals looking to enter the field. It covers the essential skills and knowledge required to succeed in this industry.

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