Data Science & the need for it

Data Science involves the retrieval of a large collection of raw data belonging to an enterprise, and processing it to get meaningful information that can be used to acquire knowledge consisting of business value for the enterprise.

The importance of Data Science stems from the challenges brought about by big data. Traditional systems have structured data which makes it easier for enterprises to analyze the data using Business Intelligence (BI) tools. But as the volume of data gathered by an enterprise increases in size, the data tends to become unstructured, thereby making Business Intelligence tools inefficient for processing the data. Data Science tools like R, Weka & RapidMiner have been introduced to process huge amounts of data. 

Examples of Implementation of data science concepts-

1. Data Science algorithms are used by search engines like Mozilla Firefox and Google to process our queries & display search results.
2. WhatsApp Web, which allows you to access the app through your computer requires the phone camera to scan a QR code on your WhatsApp web page. This scanning process involves image recognition, which is done using data science algorithms.
3. Speech recognition technologies like Alexa, Cortana & Siri also use data science concepts.
4. Email services such as Gmail & Yahoo use data science techniques for spam filtering.
5. In the field of medicine, disease prediction in human bodies is done using data science concepts. Rate of heart beat & blood glucose levels that are calculated through wearables on the human body like ‘Fitbit’ also employ data science concepts. 

What does one learn in a data science course?  

The following technologies are covered in a data science course: –

1. R programming- R programming is a language used for statistical computing.
2. Hadoop- Hadoop is a software that uses distributed computing to do massive & complex computations.
3. Python- Python is a general purpose programming language. 
4. Minitab- Minitab consists of statistic packages that are used in statistical analysis. 
5. XL Miner- XL Miner is used in the data mining process.
6. Apache Spark- Apache Spark is an analytics engine which is used for machine learning & big data.
7. SAS- SAS is a software used for predictive analysis.
8. Tableau- Tableau technology is used to prepare interactive & appealing reports using charts & graphs.

Prerequisites for learning data science

Data science can be learned by people from diverse educational backgrounds. To do well in the course, one needs to be strong in the fundamentals of mathematics like algebra & calculus, & statistics & probability. 

Job opportunities in data science

Data science professionals have a huge demand in the job market & the demand is going up day by day. Once you master data science, you can take up the following 4 roles: –

1) Data Analyst

2) Data Engineer

3) Machine Learning Engineer

4) Data Science Generalist

NOTE: To get hired, a Data Science Generalist requires years of experience.

Resource box

If you are looking to take up the data science course in bangalore. 360DigiTMG provides more than 160 hours of training by highly experienced faculty & you will also be given rigorous assignments to sharpen your skills.

Navigate Address:

360DigiTMG – Data Science, Data Scientist Course Training in Bangalore
No 23, 2nd Floor, 9th Main Rd, 22nd Cross Rd, 7th Sector, HSR Layout, Bengaluru, Karnataka 560102
1800-212-654-321