TOC
Understanding Data Science & Analytics

• Introduction
• Definition of data science & data analytics

Key Differences between data science & analytics

• Scope
• Focus
• Processes involves
• Methdologies

Techniques & tools for data Science & Analytics

Data science techniques

• Statistical modelling
• Machine learning algorithms
• Deep learning techniques
• Natural language processing (NLP)

Data analytics techniques

• Regressiion analysis
• Cohort analysis
• Cluster analysis
• Time series analysis

Skills Required for Data science & Analytics
Data science

• Programming knowledge
• Statistical knowledge
• Data wrangling and management tools
• Algorithmic knowledge

Data analytics

• Tableu
• Microsoft BI
• Zoho analytics

Conclusion

Understanding data science & analytics

Data science, as the name suggests, is the study of data, which is useful for organizations to make accurate decisions. This also involves processing the raw and unstructured data to solve business problems and predict future trends. Data science is a combination of mathematics, computations, statistics, programming, etc. This field is largely related to AI and one of the most in-demand skillset.

Data analytics, on the other hand, is the process of analyzing large data sets to make conclusive insights. This is derived from analyzing existing or past data to get immediate results. In simple terms, data analytics helps to convert the data numbers into a simple story, making it easier for businesses to find patterns and find clear conclusions.

In this blog, we will explore both data science and data analytics, key differences, skill sets, and techniques. Let’s dive straight into it.

Below is a comparative view on Data Science & Data Analytics

Key Differences in Data Science & Analytics: scope, focus, methodologies, techniques and tools

Focus point and the process involved

Data science is a broader term that encompasses areas like data analytics, data engineering, and ML. Both fields are closely related, as data act as the main point for deriving insights. The primary focus is on building newer models to predict future outcomes by exploring raw data. Whereas data analytics primary focus falls on getting patterns/insights based on the past data for current decision-making.

The processes involved in data analysis goes like this: a business questions, what exactly is a business looking for (defining clear objective). Then, collecting the data, cleaning it. Once its cleaned, analyzing those data using statistical techniques which will help discover patterns. Next step involves visualizing the data which is analyzed thus making it easier to understand.

Source: Data camp

Data analytics is focused more towards discovering patterns/insights based on the past data for today’s decision making. The process involves in data science is data collection, cleaning, data analysis (where we find out the patters). After analyzing the patterns, the major step involves building a model using ML algorithms. The last step involves model deployment and monitoring the performance.

Techniques Used in Data science & Analytics

Data Science

Data science techniques involve advanced statistical modelling (regression analysis, time series analysis etc.), machine learning algorithms, deep learning techniques, big data techniques and NLP.

Machine learning algorithms – Machine learning algorithms are set of instructions given to computer to learn from data. They are broadly classified into 3 types,

• Supervised learning – The algorithms are learned from both input output data.
• Unsupervised learning – Where the algorithms get answers from unlabeled data, and it tries to figure out patterns.
• Reinforcement learning – This is where the algorithms get trained based out on interacting with familiar environment.

Deep learning techniques – Deep learning techniques are algorithms that learns from neural networks. Some of the examples of deep learning techniques include, auto-encoders, GANs, reinforcement learning etc.

NLP – Natural language processing is a field where; algorithms extract information based on text. It includes computation linguistic, deep learning, and statistics. NLP plays a huge role in data science as it helps to understand human language and collect information’s based on unstructured texts.

Data Analytics
The different types of data used for data analysis include big data, meta data, and real time data.

Big data – big data is the data that is large and complex. It seems to be difficult often to process them because of the complexities.

Meta data – Meta data is the data that provides information based on other data, such as a file, folder or an image.

Real-time data -A data that is acquired then and there itself(live).

The techniques used for analyzing overall data sets includes,

1- Regression analysis – Regression analysis means to find out any correlation between data variables. There are two variables that we would be looking for: dependent variable (the variable that we are predicting/measuring for), and independent variable (these are the variables that will have an impact on dependent variables.

2. Cohort analysis –   Cohort analysis is based on the categorizing users on specific characteristics. For instance, the date and time they signed up, products they purchased etc. This helps in analyzing each cohort with different characteristics making it easier of the analyst.

3. Cluster analysis – cluster analysis in simple terms means sorting the data into groups. In a dataset there would be different data points, clustering them into similar and dissimilar groups will help analyst to understand how these data are spread across and will make it easier to work with them.

4.Time series analysis – Time series, as the name itself suggest is the analyzing of data based over a period of time (for example weekly, monthly etc.) It involves looking at time-related trends

Skills Required for Data science & Analytics

Data Science

The basic essential skills required for a data scientist include,

Programming knowledge – Should have the knowledge of programming languages such as Python, R which will help in analyzing large sets of data.

Statistical knowledge – For understanding machine learning models algorithms, it is important to understand statistics through which liner regressing analysis can be done.

Data wrangling and management tools – A data scientist should know how to clean the data, which is called data wrangling. They should also possess the knowledge in database management tools such as SQL, MongoDB etc.

Algorithmic knowledge – This is the base knowledge on algorithms such as machine learning and deep learning. Some of the machine learning algorithms include, linear regression, logistics regression, knowledge tree, random forest algorithms.

Data Analytics

The skills required for data analysts majorly differ on visualizing and interpreting the data. They should have the knowledge on the common data visualization tools. Some of the common data visualization tools are,

Tableau – A data visualization tools that helps in cleaning, formatting, preparing the data. It presents data visually for users clear understanding.

Microsoft BI – This is a very common tool used across organizations dues to its easy-going features such as, customized visualizations, helps in generating questions in plain language.

Zoho analytics – One of the main features that stands out in Zoho analytics is the wonderfully looking visualized data presentation. It also comes with several AI features and file formatting features, such as spreadsheet, MS word, excel etc.

Concluding Thoughts

In today’s data driven world, every organization is looking up for skills in both data science and data analytics. Businesses has huge number of data and to interpreting it will help in making right decisions for the organizations.

As discussed above data analytics is huge field, that focuses mainly into providing future solutions with model building. Whereas data analytics involves studying data to identify patterns based on which the models can be built. The key differences we looked upon is the scope, methodologies, objectives and skill sets.

Prescience Team

Prescience Team