Data Science Training

Data Scientist is the best job of the 21st century – Harvard Business Review
Global Big Data market to reach $122B in revenue in 6 years – Frost & Sullivan
The number of jobs for all USA data professionals will increase to 2.7 million per year – IBM 

The demand for Data Scientists is far greater than the supply of them. This is a serious problem in a data-driven world that we are living in today. Most of the organizations are ready to pay top-dollar salaries for professionals with the right Data Science skills. At Web Age, we offer the best data science training courses that are regularly delivered to our Fortune 500 clients. Our Data Science training courses will provide you with all skills needed to master Data Science. All this means that you can fast track your career to take on more lucrative and promising job roles and take your career to the next level.

Data science is a new discipline emerged in response to the challenges of analyzing Big Data.

Data science focuses on extraction of knowledge and business insights from data.

It does so by leveraging techniques and theories from many applied and pure science fields such as statistics, pattern recognition, machine learning, data warehousing, data visualization, scalable and high performance computing, etc.

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics and machine learning.

A Data Scientist will look at the data from many angles.

Data-driven organization establish the following three data-related roles which are highly interconnected:

Data Scientist:

Someone who uses existing data to train machine learning (ML) algorithms to make predictions and/or generalize (take actions) on new (never seen before) data; practitioners in the field apply scientific experimentation techniques when working with data trying out different ML models.

Data Analyst:

Someone who uses traditional business intelligence (BI) tools to understand, describe, categorize, and report on the existing data

Data Engineer:

Most of these activities fall under the category of ETL (Extract, Transform and Load) processes and are carried out in support of the above two roles with their data needs

A data scientist is a professional working in the field of data science.

The data scientist as a job title appears to become more popular and requires a more diverse range of skills in various areas than those of (business / data) analyst.

Practitioners in this field must have:

• practical training in statistics and machine learning
• good programming skills
• hands-on experience with scalable computation
• ability to apply scientific experimentation techniques when working with data and trying out different models
• ability to communicate with a variety of non-technical stakeholders
• domain knowledge

Today, every company is hiring Data Scientists. Here are some of the top companies hiring Data Scientists: Google, Amazon, Microsoft, IBM, Facebook, Walmart, Visa, Target, Bank of America and others.

There are multiple paths to becoming a Data Scientist. There are a set of tools that are being extensively used by a Data Scientist like the programming languages of R and Python. The person should be well aware of data analytics and statistical packages. He should also be aware of Big Data, Hadoop, and Spark which can be very useful for a Data Scientist. When the data is converted into business insights, the Data Scientist is supposed to have a good knowledge of various visualization and reporting tools. He should be firmly grounded in various aspects such as coming up with compelling visualizations, charts, maps and reports that can help anybody to understand the data. See our Big Data Training page for information on all of our training for Big Data, including Hadoop, Data Science, AI and Machine Learning Courses and NoSQL. Big Data Training and Courseware

Data science is an extremely lucrative and fulfilling career option, and a Web Age Data Science Training Course can be a great way to get started in the field. The following career options are available for individuals with Data Science Training:

​​Data Analyst: A data analyst is often considered a junior data scientist. They’re required to understand programming, machine learning, data visualization, and statistics. This is an entry-level position, and can get you started on a career path in data science.

Data Engineer: Data engineers build out the systems and data analysis platforms. They typically have backgrounds in software engineering, and are able to build and code databases and write complex queries. This is an advanced data science position.

Data Architect: Much like a data engineer, data architects create the rules, policies, and models that govern how a company collects data. Their primary goal is to make it easier for individuals to access and interact with systems. This is also considered an advanced data science position.

There is an increasing demand for skilled data scientists across all industries, making these data science training courses well-suited for participants at all levels of experience. We recommend our Data Science Training for the following professionals:

• IT professionals looking for a career switch into data science and analytics
• Software developers looking for a career switch into data science and analytics
• Professionals working in data and business analytics
• Graduates looking to build a career in analytics and data science
• Anyone with a genuine interest in the data science field
• Experienced professionals who would like to harness data science in their fields

In a nutshell, Python is better for for data manipulation and repeated tasks, while R is good for ad hoc analysis and exploring datasets. R has a steep learning curve, and people without programming experience may find it overwhelming. Python is generally considered easier to pick up.

Data Science, Machine Learning, AI?  Machine learning (ML) is a subset of data science that uses existing data to train ML algorithms to make predictions or take actions on new (never seen before) data ◊ Existing (training) data can be either labeled (classified by humans) or unlabeled.

ML is also sometimes being referred to as data mining or predictive analytics  Data science includes, in addition to ML, statistics, advanced data analysis, data visualization, data engineering, etc.

Artificial Intelligence (AI) aims at automating / augmenting / substituting complex human activities through a number of specialized computer assisted solutions.

Some of the solutions are based on deep learning through neural networksData Science, Machine Learning, AI? Machine learning (ML) is a subset of data science that uses existing data to train ML algorithms to make predictions or take actions on new (never seen before) data.

Existing (training) data can be either labeled (classified by humans) or unlabeled ML is also sometimes being referred to as data mining or predictive analytics.

Data science includes, in addition to ML, statistics, advanced data analysis, data visualization, data engineering, etc.

Artificial Intelligence (AI) aims at automating / augmenting / substituting complex human activities through a number of specialized computer assisted solutions.

Some of the solutions are based on deep learning through neural networks

• Build correlation models based on user requests / searches / product reviews (or any other data collected from uses) to predict users’ choices
• Engage user data in a feedback loop in which it contributes to improving Company’s products and services
• Develop a new customer segmentation model for the marketing department Recommendation systems (to facilitate cross-selling)
• Sentiment analysis
• Fraud detection