Data Science For Beginners

Today, data scientists are overtaking other professionals like programmers and web developers because of the need for successful companies to make data-driven decisions. Data science simply uses data both unstructured and structured with scientific procedures and methods, large systems that derive information from the data using algorithms, to give actionable insights suited to a wide variety of application domains.

Advantages of Data Science:

The advantages of using technology for data science and analytics are:

• Data is the critical ingredient to gain business advantages using the right tools of data science.
• It can help detect fraud and prevent monetary losses.
• Data science allows building intelligent algorithms and smart machines.
• It enables faster business decisions based on data-driven insights.
• Data science can help you learn how to use market sentiment analysis to gauge brand loyalty, customer preferences, etc., and suggest the best segment for any product, thus helping your business.

Components:

Data science encompasses these vital components:

• Statistics help analyze, format, and collect large volumes of numerical data to arrive at useful insights.
• Visualization techniques churn through large amounts of data, making them into smaller and digestible visuals that are understandable.
• Machine Learning(ML) is the study and building of self-learning algorithms that make predictions and provide foresight of trends, future data, etc.
• Deep Learning is ML’s method of selecting a model for analysis by the algorithm, which then can achieve self-learning and artificial intelligence accuracy very rapidly.
• Data Science processes that make sense of all the above work on data to provide the information as actionable predictions, insights, etc.

Data Science Processes Involved:

Here is how the chain of data science processes takes place in any business:

1. Data Discovery: Here, the process of acquiring data from external and internal sources like web server logs, social media channels, using datasets from the Census reports, or data streamed by APIs are collected to answer the questions posed by the business operations.
2. Data Preparation: Most often, the raw data obtained has inconsistencies in formatting, blank columns, missing values, and more, which should be cleaned and reformatted through exploring databases, processing, formatting, and reconditioning such raw data.
3. Data Model Planning: You determine and plan out the techniques and methods to correlate parameters and input variables. You will need to be good at mathematics and use statistical formulae, SQL analysis queries, data visualization tools, and languages like Python, R, Hadoop, SAS, etc., to handle Big Data.
4. Building the model: A data scientist makes consistent, well-formatted data distributions and visualized datasets for testing and training the data model using techniques/tools like classification, association, clustering, and more to the training dataset before testing it against a standard “testing” dataset.
5. Switching to operations: Once the final baseline model is ready, it is deployed after testing into an environment of real-time production.
6. Reporting the results: Post-deployment and testing, the key stakeholders are informed of the deployment and findings on project failure or success depending on the model inputs and its predicted results.

Conclusion:

A data science project portfolio, a data science certification, and hands-on experience from reputed institutions can help you land a lucrative job with endless career scope. OdinSchool’s Data Science Online Course weekend classes, tie-ups with reputed institutions, exhaustive practical work on industry-relevant projects, mentors, and industry-drawn faculty to help you hit the ground running. Enroll today!