Data science may be defined as a combination of mathematics, business acumen, tools, algorithms, and machine learning approaches that aid in the discovery of hidden insights or patterns in raw data that can be used to make important business decisions. Statistics, Data and Domain knowledge are important aspects of Data Science. We often wonder about data scientists and what do they really do.
Data science, AI, and machine learning are becoming increasingly important to businesses. Organizations that want to stay competitive in the age of big data, regardless of industry or size, must build and execute data science skills quickly or risk being left behind.
We've progressed from dealing with tiny collections of structured data to enormous amounts of semi-structured and unstructured data from a variety of sources. When it comes to analyzing this huge amount of unstructured data, typical Business Intelligence tools fall short. Data Science includes more complex tools for working with enormous amounts of data from a variety of sources, including financial records, multimedia files, marketing forms, sensors and instruments, and text files.
Who are Data Scientists and What do Data Scientists Do?
Data science is a highly multidisciplinary area that deals with a wide range of data and, unlike other analytical fields, tends to focus on the big picture. The goal of data science in business is to provide intelligence about consumers and campaigns, as well as to assist companies in developing solid plans to engage their audiences and sell their products. Big data, or vast volumes of information acquired through various collecting procedures such as data mining, requires data scientists to rely on innovative ideas. So let us find out what does a data scientist do.
Through forecasting models, data scientists analyze data and information to generate important insights that help organizations build their businesses in the correct direction. Analyzing huge data sets of quantitative and qualitative data is one of the primary duties. These individuals are responsible for designing statistical learning models for data analysis and must be familiar with statistical tools. They must also be knowledgeable enough to develop complicated prediction models.
What Do You Need to Be a Data Scientist?
In order to execute a wide range of exceedingly complicated planning and analytical activities in real-time, data scientists often require a sufficient educational or experiential background. While each profession may have its own set of requirements, most data science jobs require a bachelor's degree in a technical discipline at the very least. A bachelor’s degree in IT, computer science, engineering, math, or business is necessary. One needs a wide variety of technical and soft skills to become a data scientist. Let us see what skills do you need to be a data scientist.
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Required Skills for a Data Scientist
Data science necessitates familiarity with a variety of big data platforms and technologies, such as Hadoop, Pig, Hive, Spark, and MapReduce, as well as programming languages like SQL, Python, Scala, and Perl, and statistical computing languages like R.
Data mining, machine learning, deep learning, and the ability to combine organized and unstructured data are among the hard skills necessary for the position. Modelling, clustering, data visualization and segmentation, and predictive analysis are only a few of the statistical research approaches that are required. So what does it take to become a Data Scientist?
1. Concepts of Mathematics and Statistics
Statistics and Mathematics are must know concepts for data scientists. A solid mathematics and statistical foundation is required of every good Data Scientist. In order to aid in generating suggestions and judgements, any firm, particularly one that is data-driven, will require a Data Scientist to be conversant with different statistical approaches, such as maximum likelihood estimators, distributors, and statistical tests.
It is necessary to understand the concepts of descriptive statistics such as mean, median, mode, variance, and standard deviation. Then there are probability distributions, sample and population, CLT, skewness and kurtosis, and inferential statistics, such as hypothesis testing and confidence intervals. Calculus and linear algebra are both important since machine learning algorithms rely on them.
2. Programming knowledge
Strong knowledge of Programming knowledge is important for Data Scientists. Regarding the work data scientists do, they deal with digital data. A Data Scientist needs excellent programming abilities to progress from the theoretical to the creation of real applications. R is a statistical analysis and visualization language, whereas Python is a general-purpose programming language with various data science packages and quick prototyping. Julia combines the finest of both worlds while also being speedier.
If you want to learn Python for Data Science, you can try this Python with Data Science online course.
3. Analytics and Modeling
Data must be usable so as it be implemented for any purpose. Data Analytics is useful for investigating the data. Data analytics approaches can uncover trends and metrics that would otherwise be lost in a sea of data. This data may then be utilized to enhance procedures in order to boost a company's or system's overall efficiency.
Data modelling is the process of assigning relational rules to data. A Data Model simplifies data and turns it into meaningful information that businesses may utilize for decision-making and planning.
These are important processes in the whole Data Science process.
4. Data Visualization and Analysis
Understanding the data is important. Cleaning, converting, and modeling data to identify usable information for corporate decision-making is characterized as data analysis. Data analysis' goal is to extract usable information from data and make decisions based on that knowledge.
Data Visualization is an important part of Data Analysis. The presenting of data in a pictorial or graphical style is known as data visualization. It allows decision-makers to see analytics in a visual format, making it easier for them to comprehend difficult topics or spot new patterns. You can take the notion a step further with interactive visualization by employing technology to drill down into charts and graphs for additional detail, modifying what data you see and how it's handled dynamically.
Some powerful visualisation tools are Microsoft Power BI and Tableau. Python libraries like Matplotlib and Seaborn can also be used for data visualization.
5. Machine Learning
Machine learning is a must-have skill for any data scientist. Predictive models are created using machine learning. Machine learning is an area of computer science that looks at ways to get computers to solve problems without having to be explicitly taught to do so. This field comprises a wide range of techniques that are often classified as supervised, unsupervised, or reinforcement learning. Each of these ML kinds has advantages and disadvantages. Learning takes place when algorithms are applied to data. Each of these machine learning algorithms uses a distinct algorithm. Algorithms are instructions for performing a procedure in machine learning. They run on data in order to recognize patterns and "learn" from them.
Scikit-learn, Theano, TensorFlow are some of the popular ML libraries.
Python is a good tool to build Machine Learning models, and you can learn Python online for Data Science. You can check out this Knowledgehut Python with data science course course for learning Python for Data Science.
6. Deep Learning
Traditional Machine Learning has certain limitations. Deep learning is a branch of machine learning that teaches a computer to do human-like tasks including speech recognition, picture recognition, and prediction. It enhances the capacity to use data to categorize, recognize, detect, and characterize. Deep learning is gaining popularity as a result of the recent buzz around artificial intelligence (AI).
Some popular Deep Learning libraries which data scientists must know are Pytorch, Keras etc.
7. Data Storytelling
Data storytelling is the most effective method for using data to generate new knowledge and new decisions or actions. It is an integrative approach that draws on knowledge and abilities from a variety of disciplines, including communication, analysis, and design. It is used to solve a wide range of problems and is practised in many disciplines. Good data storytelling is an important skill, which all data scientists must have.
8. Big Data
Big Data is an application of data science in which the data volumes are massive and handling them poses logistical issues. The main challenge is gathering, storing, extracting, processing, and interpreting information from these massive data sets in an effective manner.
Physical and/or technical restrictions make processing and analysis of these massive data collections difficult or impossible. As a result, specialized approaches and tools (such as software, algorithms, parallel programming, and so on) are necessary.
Big Data is the umbrella phrase for these massive data collections, specialized approaches, and tailored instruments. It's frequently used on huge data sets to do general data analysis and discover trends, as well as to build prediction models.
Important big data tools include Hadoop, Hive, Spark etc.
9. Communication Skills
Of course, every data science job needs technological expertise in order to collect, clean, and analyze data. However, it's equally critical to remember why you're doing this. When you're assigned a project, take a moment to consider how valuable it is to the firm and how it fits into the bigger plan.
Data can't talk unless it's been manipulated, thus a good Data Scientist must be able to communicate effectively. Communication may make all the difference in the result of a project, whether it's describing the processes of a project to the team or presenting a presentation to corporate leadership.
10. Business Acumen
Understanding the business of a company is very important to proceed with Data Science projects. Data scientists must completely comprehend the business's core objectives and goals, as well as how they affect their job. They must also be able to develop solutions that satisfy those objectives in a cost-effective, simple-to-implement, and widely adopted manner.
Data Scientist Role & Responsibilities
What does a data scientist do on a daily basis? Let us understand the Data scientist role & it’s responsibilities.
Data Scientists must completely understand the business's core objectives and goals, as well as how they affect the work they undertake. They must also be able to develop solutions that satisfy those objectives while being cost-effective, simple to deploy, and widely adopted.
The roles and responsibilities of Data Scientists are:
- Identifying the data sources and gathering data
- Improving data gathering techniques to capture all pertinent information for the development of analytical systems
- Data Mining and Data Extraction
- Data Cleaning and Data processing of structured and unstructured data
- Data processing, cleaning, and validation to ensure data integrity for analysis
- Analyze data to improve product development, company strategy, and marketing approaches.
- Analyzing massive volumes of data to discover patterns and solutions.
- Selecting features, creating and optimizing classifiers with machine learning tools.
- Develop complete analytical solutions, from data gathering to presentation.
- Training and validating Machine learning and Deep Learning models.
- Identify possibilities for efficiently leveraging corporate data to drive business choices and solutions with stakeholders.
- Work with the business and IT teams to achieve goals.
- Create a testing framework and conduct A/B testing with data utilising their various data models, comparing the results of the A/B testing.
- Perform Analytical study on current data, and produce the results in the form of reports and future company goals.
Data Analyst vs Data Scientist: What’s the Difference?
While both data analysts and data scientists deal with data, the primary distinction is in what they do with it.
Data analysts evaluate big data sets for trends, generate charts, and create visual presentations to assist corporations in making better strategic decisions.
Data scientists, on the other hand, use prototypes, algorithms, predictive models, and specialized analyses to create and build new processes for data modelling and production.
Let us understand the key differences between these two roles.
The skills required for these roles have many common aspects, but there are many differences.
|Data Analyst||Data Scientist|
|Good understanding of statistics and probability, plotting, charting and data representation.||A solid understanding of calculus, linear algebra, statistics, and probability.|
|Knowledge of Python programming, SQL and Data Visualization tools like Power BI and Tableau.||Proficient with Python, SQL, R, SAS, MATLAB, and Spark.|
|Data Storytelling and Exploratory Data Analysis||Cloud Computing, Machine Learning, Deep Learning|
Roles and Responsibility
A data scientist's job is to translate knowledge into a business story using strong business acumen and data visualization abilities, whereas a data analyst is not required to have strong business acumen and advanced data visualization skills.
A data scientist investigates and analyses data from several unconnected sources, whereas a data analyst often studies data from a single source, such as a CRM system. A data analyst will answer questions posed by the business, but a data scientist will develop questions whose answers are likely to help the business.
|Data Analyst||Data Scientist|
|Gather Data from numerous databases and warehouses, then filter and clean the data.||Data Scientists perform Ad hoc data mining to collect vast amounts of structured and unstructured data from a variety of sources.|
|Gather, store, alter, and retrieve data from RDBMS like MS SQL Server, Oracle DB, and MySQL, write complicated SQL queries and scripts.||Design and assess complex statistical models from large amounts of data using a variety of statistical methodologies and data visualization approaches.|
|Use data analytics tools to understand new metrics to uncover previously unknown aspects of the business.||Create models for problem-solving and solving tasks with AI.|
Two in-demand job roles are data analyst and data scientist. Many students and working professionals are interested in pursuing these careers. A Data Analyst position is better suited for people who wish to begin their analytics career. For people who wish to construct sophisticated machine learning models and leverage deep learning techniques to make human work easier, a Data Scientist role is advised.
Data Scientists work in various companies. The majority of businesses are using data science to help them expand. Data scientists are in high demand not just in the IT industry, but also in other important industries such as FMCG, logistics, and more.
Data scientists are professionals at inspecting data to detect trends, as well as programming and data modelling abilities. They are experts at machine learning and can design new procedures for visualising data in addition to data analyst tasks. They mostly handle problems using diverse ways. They examine the data and provide questions and answers that may help to address any lingering business issues.
A Field with Endless Possibilities
Data Science is now regarded as one of the most rewarding professions accessible. Data scientists are needed by companies in all major industries and sectors to assist them acquire important insights from massive data. The demand for highly competent data science specialists who can work in both the business and IT worlds is on the rise.
Because data science is a relatively new profession, the road to becoming a data scientist is not well defined. Data scientists often come from a variety of disciplines, including mathematics, statistics, computer science, and economics. With this article, we were able to understand what data scientists do and what skills you need to become a data scientist.