Bangalore is home to some of the most prestigious universities in the world in terms of Data science courses. These institutions include INSOFE, International Institute of Information Technology, IIK (Indian Institute of Knowledge hub), Peopleclick, Data Science, Data scientist & Data Analytics Courses, Business Analytics Training Institute Bangalore, Indian Institute of Management Bangalore, etc. The top skills that are needed to become a data scientist include the following:
- Programming
- Big Data
- Statistics
- Machine Learning and Advanced Machine Learning
- Data Cleaning
- Data Ingestion
- Data Visualization
- Unstructured data
1. Programming:
Data Science is a dynamic field with ever increasing tools and technologies added to it every now and then. You should be able to choose the best programming language suited to you to tackle a specific kind of problem. Apart from mathematical skills, it is important to be proficient in one or more programming languages. The programming for Data Science differs from the conventional programming language in the sense that it helps the user to pre-process, analyze and generate predictions from the data, while the other programming languages focus on software development. The main programming languages that an aspiring data scientist should be familiar with are as follows:
- R
- Python
- SQL
- Scala
- Julia
- SAS
2. Big Data:
Big Data technology centers in ways to analyze a large volume of data to reveal behavior, trends, and patterns especially related to human behavior. Big Data Analytics is in the frontiers of IT as it aids in improving business, decision making and providing the biggest edge over the competitors hence it is crucial. Therefore, it is very important to have knowledge about frameworks like Hadoop and Spark that can process Big Data.
Apache Spark is a fast and general-purpose cluster computing system designed to cover a wide range of workloads such as iterative algorithms, interactive queries, batch applications, and streaming. Hadoop provides scalable, reliable, and distributed computing to solve problems including huge amounts of data.
3. Statistics:
Statistics is a broad field which is defined by Wikipedia as the study of the collection, analysis, interpretation, presentation, and organization of data. The minimum skills needed to make better business decisions from data are descriptive statistics and probability theory. Machine learning requires understanding Bayesian thinking which is the process of updating beliefs as additional data is collected. Key concepts in statistics include:
- probability distributions
- statistical significance
- hypothesis testing
- regression.
4. Machine Learning and Advanced Machine Learning:
Machine Learning focuses on the development of computer programs in such a way that they can access data, analyze it and manipulate it so that it provides the ability to systems to automate the experience without the need of programming. Machine Learning requires a better understanding of neural networks, reinforcement learning, adversarial learning, etc. and can be considered as a subset of Artificial Intelligence. The different types of Machine Learning techniques include the following:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
It is recommended to have good knowledge of various Supervised and Unsupervised learning algorithms such as:
- Random Forest
- Clustering (for example K-means)
- Logistic Regression
- K Nearest Neighbor
- Linear Regression
5. Data Cleaning:
Since the data that the data scientists work on is highly sensitive and important, it is important that the data is correct and accurate before data scientists analyze it and therefore, a considerable amount of time and effort is spent to ensure this. Incorrect or inconsistent data leads to false conclusions hence it has a high impact on the quality of the results. Data quality is defined as validity, accuracy, completeness, consistency, and uniformity of data. The workflow followed for data cleansing includes the following steps:
- Inspection
- Cleaning
- Verification
- Reporting
6. Data Ingestion:
Data Ingestion is the process of accessing and importing data from several different sources into our system for analytics. The sources of data are your IoT Smartwatch, social networks, customer portals, messengers, forums, etc. These are the most common examples of data ingestion :
- HTTP POST
- Download file from FTP
The different data ingestion tools available :
- Apache Flume
- Apache NIFI
- Syncsort
- Apache Flume
- Apache Kafka
- Gobblin
- Heka
7. Data visualization:
Data visualization tools provide a better and accessible way to enable decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. It helps to see and understand trends, outliers, and patterns in data by using visual elements like maps, graphs, and charts. By using technology to drill down into charts and graphs for more detail, we can interactively change what data you see and how it’s processed through visualization. A good and effective data visualization tool makes large data sets coherent and some of these tools are as follows:
- Tableau
- Infogram
- ChartBlocks
- Datawrapper
- Plotly
- RAW
- Visual.ly
8. Unstructured Data:
Unstructured data can be defined as data that cannot fit neatly into a database and does not have a recognizable structure. It does not follow the conventional data model like Word documents, email messages, PowerPoint presentations, survey responses, transcripts of call center interactions, and posts from blogs and social media sites. Therefore, it leads to ambiguities that are difficult to identify using conventional software programs. Working with unstructured data provides a better insight into analyzing data.