A Beginner’s Guide to Data Science

Companies, regardless of size and industry, are dealing with abundant data today. They want to stay one step ahead of their competitors all the time. As Big Data gained prominence, the need to store and process it increased as well; this is where Data Science comes into play. It combines statistics, computer science, and various machine learning algorithms and tools to make valuable predictions and better business decisions. In the olden days, data was much simpler and well structured which could be processed using basic Business Intelligence tools. However, today most of it is complex and unstructured or semi-structured and therefore requires advanced analytical tools and methods to process, analyze and derive meaning from it. Data Science Training will help you master all the tools and aspects of this technology and help professionals implement extensible methodologies to get businesses right on track. 

Why should you get certified in this innovative Science?

Getting certified from the Best Data Science Bootcamps will help you gain expertise and enrich your knowledge to pursue a robust and lucrative career. This qualification will aid in making you stand out among other candidates and will increase your professional credibility. Most of the firms, big and small, are leveraging this technology, and therefore professionals with adept skills in this field are in high demand. Mastering the best practices of this domain along with the core concepts will give you an advantage in front of prospective employers. You can easily get hired by reputable and established companies like Google, Accenture, Facebook, Apple, and Amazon, among many others. 

This powerful technology is applied in various sectors and scenarios. Let us have a look at some of them:

Fraud detection and risk mitigation: The banking and finance sectors use this technology to detect fraudulent activities and reduce risk by gathering user information and applying machine learning algorithms to it. Risk modeling is another feature that this domain offers, and it helps assess the firm’s overall performance. 

Healthcare: Various fields in the healthcare sector like – medical image analysis, drug discovery, genetics and genomics, health bots or virtual assistants, and predictive modeling for diagnosis, make use of this robust technology to make great leaps.

Target Advertising: These algorithms are helpful in target advertising, i.e., displaying advertisements on websites by understanding the user’s past behavior. Digital ads have a higher CTR than regular ads and are a valuable addition to the vast digital platforms. 

Website Recommendations: Recommendations on e-commerce websites like Amazon and Flipkart are also displayed using data science with the help of innovative algorithms. These recommendations are beneficial for users and increase their experience manifold. 

Internet Search: Search engines like Google, Yahoo, Bing, etc., use this technology to process data and derive the best result for a search query in minimum time. 

Let us move onto the technical skills required before joining a Data Science Bootcamp.

1. Python

Python is the simplest and most extensible of all programming languages. Being flexible and portable, Python can be used as an end-to-end solution for processes ranging from data mining to running the embedded systems. Python’s library called Pandas is used to manipulate and read information, as well as visualize it. 

2. R programming

R is an important tool for purposes like data mining and analytics. It is a programming language widely used by a Data Science Programmer for statistical computing and graphical techniques. R processes raw data and churns out useful insights from it, and provides an intensive environment for functions on arrays, matrices, and vectors.  

3. Hadoop

Hadoop is an open-source platform used to handle large amounts of information and solve all its problems. It offers tools like MapReduce, PIG, and Hive for efficiently running massive data sets. It is a highly scalable and fault-tolerant solution for Big Data, and the ecosystem it presents to its users has been lauded for reliability and cost-effectiveness. 

4. SQL database

Relational Database Management System (RDMS) is a database solution that stores information in tables and is used in relation to other data sets. Structured Query Language or SQL is used to interact with this database. SQL is mainly implemented to extract data from a database and update new ones into the database. 

5. Machine Learning and Artificial Intelligence

Proficiency in ML and AI is significant in a data science professional. ML techniques like supervised and unsupervised machine learning, decision trees, and logistic regression help automate tasks like cleaning data by clearing redundancies. These are done using complicated algorithms, which help systems learn from the data and derive meaning from it without being explicitly programmed. 

6. Visualization

As data gets bigger, the visual representation of it becomes increasingly crucial for businesses in generating better leads. Tools like ggplot, d3.js, and Tableau, help in the graphical representation of vast volumes of Big Data through mediums such as charts, graphics, maps, infographics, etc. 

7. Business Strategy

An eye for understanding possible business glitches and being able to conduct analysis to arrive at constructive solutions is a critical skill in data science. This will help a Data Science Programmer in developing an infrastructure that is useful for the enterprise. 

Here are a few tools associated with the various stages of this domain that will be dealt with in detail at any programming Bootcamp:

• Data Storage

Apache Hadoop: Hadoop is an open-source framework used to manage and store large chunks of data. It is helpful in intricate computations and data processing. Apache Hadoop is highly scalable and easy to use. It provides robust modules like MapReduce and YARN. 

Microsoft HD Insights: Azure HDInsight is a Microsoft cloud platform that helps manage and process vast data. It can be blended with Apache Hadoop and Spark clusters and manages sensitive data across nodes. 

• Exploratory Data Analysis

Informatica PowerCenter: Informatica PowerCenter is used to extract data from its source, transform it as per requirement and finally load it into the target data warehouse. It provides services such as B2B exchange, Data governance, Data migration, Data warehousing, Data synchronization and replication, Integration Competency Centers (ICC), Master Data Management (MDM), Service-oriented architectures (SOA), and many more.

RapidMiner: RapidMiner is a software framework that unites data preparation, predictive analytics, machine learning, text mining, and deep learning. It can be integrated with the Hadoop framework. 

• Data Modelling

H2O.ai: This is an open-source tool that makes data modeling a much simpler task. Python is the programming language used to develop this tool and hence is easier to comprehend. H2O.ai can implement various ML algorithms, support deep learning too, and be integrated with Apache Hadoop.

DataRobot: DataRobot is an AI-based platform that helps in making accurate predictions. Algorithms like clustering, classification, regression models can be implemented efficiently using this tool. DataRobot allows parallel programming by using thousands of servers simultaneously.

• Data Visualization

Tableau: Tableau is a widely popular and known tool used for visualizing data. Raw and unformatted data is broken down into meaningful parts. This tool can connect multiple data sources and visualize large amounts of data sets to find meaning and interdependencies. You can generate customized reports and dashboards that will provide real-time information of data sets. Cross-database relations can be derived using this tool as well. 

QlikView: This is another visualization tool and is one of the most accurate of all the platforms. QlikView offers in-memory data processing features, which help in creating reports and communicating with the end-users faster. This feature also aids in finding relations and bonds in data. 

Taking up a course in this cutting-edge technology will prepare you for the competitive industry by imparting hands-on training and practical knowledge of the various aspects.If you want to gain knowledge in skills like data visualization, manipulation, predictive modeling, web scraping, and much more, then apply to Synergistic IT, one of the best data science bootcamps in California. Under the guidance of expert and competent trainers, you will create an attractive portfolio that boasts valuable abilities in the field and thereby land you the dream job you have been waiting for.