Data Science and Analytics
With the rise of Big Data, two new buzzwords have emerged in the industry: Data Science and Data Analytics. The entire world is now contributing to massive data expansion in colossal proportions, thus the term “Big Data.” According to the World Economic Forum, by the end of 2020 daily global data creation will exceed 44 zettabytes. By 2025, this figure will have risen to 463 exabytes by 2025!
Big Data includes everything we do online, such as messages, emails, tweets, user queries (on search engines), social network activity, and data created by IoT and connected devices. Traditional data processing and analysis tools cannot deal with the massive amounts of data generated daily by the digital world.
Because Big Data, Data Science, and Data Analytics are still developing technologies, we frequently use the terms Big Data, Data Science, and Data Analytics interchangeably. The fact that both Data Scientists and Data Analysts work with Big Data (large and complex datasets with a high variety, volume, and velocity) contributes to the misconception. Even after this, the difference between Data Analysts and Data scientists is obvious, fueling the dispute between Data Science and Data Analytics.
What is Data Analytics?
The act of analyzing datasets (collection of values relating to a specific subject) in order to draw conclusions about the information contained within them is referred to as “data analytics.” Data analytic techniques enable you to take raw data and extract valuable important insights from it by uncovering patterns. These patterns may be useful in improving the sales of a business by making better recommendations to a user.
Nowadays, many data analytics techniques rely on specialized systems and software that combine machine learning algorithms, automation, and other features. In research, data analysts apply data analytics methodologies, and businesses use them to guide their judgments. Data analysis can help businesses to understand their clients better, evaluate their advertising efforts, personalize content, develop content strategies, and develop new products. Finally, firms may employ data analytics to improve their bottom line and boost their performance.
Types of Data Analytics
- Descriptive Analytics – Descriptive analytics can help with event investigation. These strategies synthesize big datasets to describe outcomes to stakeholders. These tactics can assist in tracking successes and failures by establishing key performance indicators (KPIs). Many businesses use metrics such as return on investment (ROI) or others. To track success in certain industries, specialized metrics are devised. This process entails the gathering of relevant data, data processing, data analysis, and data visualization. This procedure provides crucial information about previous performance.
- Diagnostic Analytics – Diagnostic analytics can help you to figure out why things happened the way they did. These strategies are used in conjunction with more basic descriptive analytics. They take the results of descriptive analytics and delve further to find the root of the problem. The performance indicators are probed deeper to see why they have improved or deteriorated. This typically occurs in three stages:
- Recognize inconsistencies in the data. These could be unanticipated changes in a statistic or a market.
- Data pertaining to these anomalies is gathered.
- To uncover links and trends that explain these abnormalities, statistical methods are applied.
Predictive Analytics – Predictive analytics assists in determining what will happen in the future. These procedures make use of historical data to detect patterns and decide if they are likely to repeat again. Predictive analytical tools use a number of statistical and machine learning approaches, such as neural networks, decision trees, and regression, to provide significant insight into what may occur in the future. This might be used by stock consulting agencies to analyze the trends in which the prices of the stocks have crashed in the past and to advise their customers properly.
Prescriptive Analytics – Prescriptive analytics helps in determining what should be done. Data-driven decisions can be made utilizing predictive analytics insights. In the face of uncertainty, this helps firms to make educated judgments. Machine learning strategies are used in predictive analytics techniques to detect trends in large datasets. The likelihood of various outcomes can be calculated by evaluating past decisions and events.
Features of Data Analytics
- Better Decision-Making – Companies may employ data analytics insights to better guide their decisions, resulting in better results. In marketing campaigns, content creation, product development, and other areas Data analytics takes out a lot of the guesswork. It gives you a 360-degree perspective of your customers, allowing you to better understand them and address their demands. Furthermore, contemporary data analytics technology allows you to collect and analyze fresh data on a constant basis to keep your understanding up to date as conditions change.
- Increased Marketing Efficiency – You can market to your audience more successfully if you have a deeper understanding of who they are. Data analytics can also assist you to understand how well your campaigns are functioning so you can fine-tune them for the best outcomes.
You may learn which audience sets are most likely to connect with a campaign and convert with the Lotame Campaign Analytics tool. You can utilize this data to manually or automatically alter your targeting criteria or to build different messaging and creative for different categories. More conversions and less ad waste result from better targeting.
- Operational Efficiency – Data analytics can assist you in streamlining your procedures, saving money, and increasing your profits. You waste less time developing advertising and content that do not fit your audience’s interests when you have a better understanding of what they want.
This means you will spend less money and get better results from your campaigns and content strategy. Analytics can generate returns through increasing conversions, ad revenue, or subscriptions in addition to lowering costs.
- Customer Service Improvements – Data analytics can assist you to gain a better understanding of your consumers, allowing you to adjust customer care to their specific needs, provide greater personalization, and strengthen customer connections.
Your data may provide details about your consumers’ communication preferences, interests, and worries, among other things. Having a common repository for this information means that your whole customer service staff, as well as your sales and marketing departments, are on the same page.
It can also be used to detect the category of things that a particular user buys or sees and to recommend related products to them, thereby improving the business.
What is Data Science?
Data science is an interdisciplinary approach to obtaining useful insights from today’s organizations’ huge and ever-increasing volumes of data. Preparing data for analysis and processing, undertaking advanced data analysis, and presenting the results to reveal trends and allow stakeholders to make educated decisions are all part of data science. Data Science, in its general sense, is the study of data, including what is its source, what it represents, and how it may be turned into useful inputs and resources for business and IT strategy.
There are 5 major steps in the life cycle of a Data Science project. They are as given below:
- Gathering Data – The first step in any data science project is fairly straightforward: gather and receive the data you require. You would not be able to process anything if you do not have any data, to start with.
- Scrubbing Data –We must “clean” and filter the data, which is the most crucial step in the data scrubbing process. Keep in mind the adage “garbage in, garbage out,” which asserts that analysis results will be worthless if the data used was unfiltered and irrelevant. Remember the “garbage in, garbage out” principle, which states that if the data used is unfiltered and irrelevant, the analysis results will be meaningless. You must convert data from one format to another and combine everything into a single standardized format for all data in this procedure. If your data is stored as CSV files, for example, you’ll need to run SQL queries on it before you can use it with computer languages like Python or R.
- Exploring Data – Once the Data is ready to be used you’ll need to investigate the data, and before you dive into AI and Machine Learning. Your managers would often just throw you a tonne of information in a corporate or commercial atmosphere, and it would be up to you to figure out what to do with it. As a result, it will be up to you to help them in determining the business question and converting it into a data science query. You can use a variety of marketing techniques and algorithms to extract meaningful information from the data.
- Modeling Data – This is the most exciting stage of a data science project’s lifecycle. It’s “where the magic happens,” as many people would put it. One of the initial steps in data modeling is to limit the number of dimensions in your data set. Not all of your features or values are required for your model to be predicted. As a result, you must choose the relevant ones that will contribute to the forecast of the desired results.
- Interpreting Data – Simply put, data interpretation is presenting your data and delivering the results in a way that responds to the business questions you stated when you first began the project as well as the useful information you learned from data science.
Features of Data Science-
Here are the main features of Data Science-
Understanding of the Business – Even if you know a lot about machine learning algorithms and have strong statistical skills, you will be unable to construct a good model unless you understand the company. Before developing analytics to meet business demand, a data scientist must first comprehend it. The outcome, business domain knowledge becomes vital or useful.
Intuition – Even though maths is sound and foundational, a data scientist must select the proper model with the right precision, as not all models yield the same results. As a result, a data scientist must be able to determine when a model is ready to be deployed in production. They also need the ability to recognize when a production model has become stale and requires restructuring in order to adjust to changing business conditions.
Curiosity – Data science is not a brand-new discipline. It has existed in the past as well, but growth in this topic is extremely rapid. As new approaches to handling well-known problems emerge on a regular basis, a data scientist’s desire to learn about emerging technology becomes increasingly vital.
Difference between Data Science Vs Data Analytics
Data Analysts Responsibilities
Data analysts attempt to determine what questions the company needs to answer and then determine whether they can be answered with data. They must be familiar with data collection, analysis, and reporting techniques. They must recognize patterns and trends. The following are some of the most important data analyst tasks:
- Statistical techniques are used for data analysis and reporting.
- Development and implementation of databases and data collection systems
- Obtaining and storing information from primary and secondary sources
- Identifying, analyzing, and comprehending patterns or trends in massive data sets
- Cleaning and filtering data
- Working with business management and developers to identify and prioritize business and information needs
- Identifying and outlining new process improvement opportunities
Data Scientist Responsibilities
The Following responsibilities of a data scientist are-
Management – in the Data and Analytics department, the Data Scientist has a minor managerial position where he contributes to building a foundation of futuristic and technical skills in order to help with various planned and ongoing data analytics projects.
Analytics – The Data Scientist is a scientist who develops, implements, and evaluates high-level statistical models and techniques for use in the company’s most challenging issue. The Data Scientist creates economic and statistical models for a variety of issues, such as projections, classification, clustering, pattern analysis, sampling, simulations, and more.
Strategy/Design– The Data Scientist plays a critical part in the development of creative strategies to better understand and manage the company’s consumer trends and management, as well as approaches to tackle complex business problems such as product fulfillment and overall profit optimization.
Collaboration – The Data Scientist’s role is not a solitary one, and he collaborates with other data scientists to communicate problems and discoveries to key stakeholders in order to improve company performance and decision-making.
Knowledge – The Data Scientist also takes the lead in experimenting with new technologies and tools in order to develop new data-driven insights for the business at the fastest possible pace. In this case, the Data Scientist takes the initiative in evaluating and implementing new and improved data science methodologies for the company, which he then presents to top management for approval.
Other responsibilities – A Data Scientist also completes related tasks as directed by the manager.
Data Analyst Skills
The following skills are required for the job role of a data analyst
- SQL – SQL (Structured Query Language) is the industry standard database language and it may be the most important skill for data analysts to possess. Because it can handle massive datasets that Excel cannot, the language is frequently referred to as a “graduated” version of Excel. Almost every company needs someone who knows SQL, whether it’s to manage and store data, link different databases ) or develop or update database architecture entirely.
- Analysis Tools like Microsoft Excel – A spreadsheet is probably the first thing that comes to mind when you think of Excel, but it has a lot more analysis capability behind the hood. While a programming language like R or Python is better suited to dealing with large data sets, advanced Excel approaches such as building macros and employing VBA lookups are still extensively utilized for smaller lifts and quick analytics. If you work for a small company or a startup, the first version of your database can be Excel. The tool has been a cornerstone for firms in every industry over the years, thus mastering it is essential.
- Statistical Programming Languages – R or Python can do the same thing as Excel, but 10 times faster. R and Python, like SQL, can handle tasks that Excel can’t. They are statistical programming languages that may be used to do advanced analysis and predictive analytics on massive amounts of data sets. They’re also both industry-standard. To genuinely work as a data analyst, you must be fluent in at least one of these languages besides SQL.
- Critical Thinking and Problem-Solving Skills – To use data to get answers to your questions, you must first figure out what you want to ask, which may be hard. You must think an analyst to be successful as one. A data analyst’s job is to find and synthesize relationships that aren’t always obvious. You must also have good problem-solving skills.
- Data Visualisation – It’s critical to be able to tell a compelling story using data in order to convey your message and keep your audience engaged. You’ll have a difficult time getting your message through to others if your findings can’t be simply and immediately recognized. As a result, when it comes to the impact of your data, data visualization may make or break it. Analysts convey their conclusions in a clear and simple manner by using eye-catching, high-quality charts and graphs.
- Machine Learning – Machine learning has been identified as a critical component of an analyst’s toolset, as artificial intelligence and predictive analytics are two of the hottest subjects in the field of data science. While not every analyst works with machine learning, understanding the tools and ideas is essential for getting far in the profession.
Data Scientist Skills
The following are the skills required for a data scientist
- Maths and Statistics – A professional Data Scientist will have a solid mathematical and statistical background. In order to assist in making recommendations and judgments, any company, particularly one that is data-driven, will require a Data Scientist to be familiar with various statistical methodologies, such as maximum likelihood estimators, distributors, and statistical tests. Both calculus and linear algebra are important since machine learning algorithms rely on them.
- Programming – To progress from the theoretical to the creation of real-world applications, a Data Scientist needs strong programming skills. You’ll be expected by the majority of companies to be proficient in Python, R, and other programming languages. Programming with objects, fundamental syntax, and functions, flow control clauses, libraries, and documentation all fall under this category.
- Analytics and Modelling – Because data is only as good as the persons who analyze and model it, a qualified Data Scientist is expected to be well-versed in this field. A Data Scientist should be able to examine data, run tests, and construct models to collect new insights and forecast future outcomes based on a foundation of both critical thinking and communication.
- Machine Learning – While expert-level knowledge in this area isn’t always required, some familiarity will be expected. Machine learning provides critical features such as decision trees, logistic regression, and more, prospective employers will be looking for these skills.
- Data Visualisation – As a Data Scientist, you must be able to effectively communicate critical messaging and gain buy-in for offered solutions, which necessitates the use of data visualization. Understanding how to break down complex data into smaller, more digestible chunks and use a range of visual aids (charts, graphs, and more) is a skill that any Data Scientist will need to master in order to succeed in their profession.
- Problem-Solving Skills – A data scientist has good problem-solving skills. They must be able to come up with a solution to the problem at any point in development.
Once you understand the differences between data analytics and data science, as well as what each career entails, you can begin analyzing which path is the best fit for you. When deciding which path is best aligned with your personal and professional goals, three major factors should be considered.
Your Personal Background – Data analysts analyze massive data sets to discover trends, build charts, and create visual presentations to help businesses make better strategic decisions. To align their education with these jobs, analysts frequently pursue an undergraduate degree in a science, technology, engineering, or maths (STEM) discipline, as well as an advanced degree in analytics or a related subject. They also want to know if you have any maths, scientific, programming, database, modeling, or predictive analytics experience.
Data scientists, on the other hand, are more concerned with the creation and implementation of new data modeling and production methods. Because they use a variety of approaches to comb through data, such as data mining and machine learning, advanced degrees in data science, such as a master’s in data science, is required for professional advancement.
Your passions – Data analysts enjoy working with numbers, statistics, and programming. They almost entirely work in databases as gatekeepers for their organization’s data, uncovering data points from complex and frequently diverse sources. Data analysts must also be well-versed in the industry in which they work.
Data scientists must be proficient in maths, statistics, and computer science, as well as have an interest in — and knowledge of— business. If this description more closely matches your background and experience, a position as a data scientist may be the best fit for you.
Desired Salary and Career Path – The salary of a data scientist in the initial stages of a career is higher than that of a data analyst. If salary is the main motivator for you, you can consider opting for a career in data science. That being said, as one advance in their career, the salary increases exponentially and so either of the two is a good option, considering in terms of salary.
Thank you for reading this, hope this answer helps with your future career shift or your understanding of Data Science and Analytics.