With technology advancement, Big Data is offering millions of jobs worldwide. Demand for a data scientist is increasing day by day from a few decades. As we know, every economy is driven by digital activity, so data plays a major role in all industries. From manufacturing to retail, each company extremely depends upon high-quality data, that enhances the demand for data scientists. Many institutes are also found for guiding the Data Science Course In Toronto.
What Is Data Science?
Data science is a very broad concept. This encompassing everything from initial level data-wrangling positions to advanced data engineering posts requiring high-level degrees. Majority of data science posts involve some combination of organizing, storing, and analyzing data sets. Sometimes Data scientists also worked for collecting data.
The Career In Data Science And Their Responsibilities
Data scientists are a mix of computer scientists, mathematicians, and trend-spotters. Large volumes of data are used to carry out further analysis for finding trends and gain a deeper insight.
- Creation of data-driven business solution and analytics,
- Data optimization and improvement of product development.
- Use predictive modelling to increase and optimize customer experience and revenue generation, Ads targeting etc.
- Coordinating with the different functional team to implement modelling and monitor outcomes.
Data Analyst applies data to help figure out market and business trends by analyzing data to develop a blueprint of where the company stands.
- Interpreting data analysis results using statistical techniques
- Acquiring data from primary and secondary sources and maintaining a database
- Developing and implementing many strategies including data analysis, and data collection system
- Work with management to priorities information and business needs
The data engineer examines not only the data for their own business but also that of third parties for better analysis.
- Assemble large complex data sets.
- Identity design and implement internal process improvement.
- Building infrastructure that required for optimal extraction, transformation and loading of data.
- Build analytics tools that utilize data pipeline.
Data architects work with users, developers and system designers to create blueprints that data management systems use to centralize, integrate, maintain, and protect data sources.
- Develop a database solution.
- Install and configure information system.
- Analyze new structural requirements for new software and applications.
- Migrate data from legacy system to new solutions
Business Analysis works on business change requirements, evaluating the business impact of those changes, capturing, analysing and documenting requirements and supporting the communication and delivery of requirements.
- Assisting the business with planning and monitoring.
- Eliciting and orgainsing requirements.
- Validate resource requirements and develop cost estimate models.
- Create informative, actionable and repetitive reporting
A database administrator (DBA) administers all actions related to managing a successful database environment.
- Assisting in database design and updating existing database.
- Creating & testing a new database and data handling system.
- Sustaining the security and integrating the data.
- Creating complex query definition that allows data to be extracted
The Skill Required To Become A Data Scientist
Database knowledge is required to store and analyse data. Some of the tools like Oracle database, SQL Server, MySQL, and TERADATA are used to store big data.
Learning Statistics, Probability or say the Mathematical analysis is a science concerned with developing and studying methods analysing, interpreting and presenting empirical data.
There are many programming languages but for the Data Scientist, one must have proficiency in any of the languages. Such as R, Python, and SAS are very important to perform the analysis.
R is a free software environment for statistical computing and Graphs. The best part of R is that it supports all machines learning algorithm for Data analysis like regression, association and clustering etc.
Python is general purpose programming language that works as an open source. Python use libraries like NumPy, SciPy for data science.
SAS has the capability to mine, alter, manage and retrieve data from a variety of sources. SAS can perform statistical analysis on data effectively.
The next skill required for Data Scientist is Data Wrangling. This involves Cleaning, manipulating and organising the data for effective use. Some of the tools used in data wrangling are Arc, Python, Flu, and Scoop.
Data Visualisation involves integrated different datasets, analysing models and visualising them, in the form of diagrams charts and graphs. Tableau, Qlik View, Power BI, Google Data Studios are the name of few tools used for Data Visualisation.
Big Data is a term to describe large and complex data which cannot be dealt with traditional data processing software. Some of the tools or software used for big data are Apache Spark, Hadoop, Talend, Tableau, Splunk, Cassandra, Pentaho.
Machine Learning provides a system with the ability to automatically learn and improve from experience without being explicitly programmed to. Machine Learning can be achieved through various algorithms such as regression, Naive Bayes, SVM, K means a cluster, KNN and decision tree algorithms are the few examples.
Choosing the path of data scientist is a challenging and rewarding career. There is a great demand for professionals having a great expertise in data science all over the world. Just opt for data science Course Toronto and a promising career and the desired success will head to your way sooner or later.