What is Data Science for Certified Analytics Professional Training
Data Science means getting valuable insight from the data by means of data inference and exploration. Data Science uses automated methods to analyze a huge amount of data and extract knowledge from them in various forms either structured or unstructured. Data Science combines various aspects of statistics, computer science, maths, and visualization. In research, data science is used to convert the huge data into new insight and new knowledge. It is of no secret that data science can help to solve the most complex problems faced by business leaders today.
Who is a Data Scientist for Certified Analytics Professional Training
Data Scientists work with data science. Data Scientist uses their analytical skill to interpret data sources, manage a large amount of data, ensure consistency of data and finally communicate the data insights. They use technology and skills to work with data. Data scientists present the data with an intelligent awareness of the effects of presenting the data. They usually produce results with dashboards instead of papers or reports.
Data Science Uses for Certified Analytics Professional Training
Certified analytics professional and Data science make company intelligent to predict their customers purchasing power and interest and then sell the products accordingly. Here is a list of applications where data science rules
- Internet Search
- Digital Advertisements
- Recommender Systems
- Image Recognition
- Speech Recognition
- Price Comparison sites
- Airline Route Planning
- Fraud detection
- Delivery Logistics
- Risk detection
- Others like Human Resource and Healthcare
Certified Analytics Professional Course Objectives
At the end of this Certified Analytics Professional course, you will be able to
- Know what is Data Science
- Know what is a data scientist
- Data and various objectives
- Differentiate between BI and Data Science
- Go through the steps of data science
- Learn about the components of data science
- Know the guiding principles and common reasons to become a data scientist
Pre Requisites for taking this Certified Analytics Professional Course
In order to take up this course, you need to have basic knowledge in statistics and have a intermediate programming experience. You should also have a strong interest in Data Science. Familiarity with R is an added advantage.
Target Audience – Certified Analytics Professional Training
The target audience of this course is analytics professionals, students and people who wanted to appear for the CAP certification with all the eligibility.
Certified Analytics Professional Training – Description
Data Science Introduction and difference between BI and Data Science
Data science is considered as an evolutionary step in interdisciplinary fields that includes computer science, modeling, statistics, and mathematics. Data science has become an integral part of competitive intelligence in businesses these days which includes data mining and data analysis. This chapter includes a brief introduction to data science and the history of data science.
Data Science Introduction and difference between BI and Data Science continued
Technological advancements have made businesses focus more on the future by analyzing the data. To improve the business companies have started to transform data into insights which are called real data science.
Whereas on the other hand companies are also using Business Intelligence activities to use the data and create charts, reports, and graphs.
To take care of these functions companies appoint dedicated BI analysts as well as data scientists. But many companies get confused between these two concepts. This chapter gives you the difference between Business Intelligence and Data Science on certain factors.
- Area of Focus
- Data Analysis and Quality
- Data Sources and Transformation
- Need for Mitigation
A contrastive chart which represents the difference between Data Science and Business Intelligence based on three factors Content/Tools, Business, and Data are also provided under this chapter.
How Data Science Work along with Acquire and Prepare Steps
The data science workflow contains four important phases or stages. They are the Preparation Phase, Analysis Phase, Reflection Phase and the Dissemination Phase. This workflow of the data science is represented using a picture in this chapter. Under this chapter, the preparation and analysis phase is explained in detail.
Preparation phase contains two steps – Acquire data, Reformat and clean data.
The first step in data science is to acquire the data and convert it into a form that is suitable for computation. Data can be acquired from a variety of sources like websites, API, software’s, physical apparatus, etc. The problems faced in this step are provenance, data management, and storage. These problems are explained in detail under this topic.
The raw data cannot be used as such for data science. Programmers reformat and clean the data so that it is suitable for further analysis. Reformatting and cleaning the data can help to make few assumptions about the data. Data integration is the hurdle faced in this step. This is explained in detail in this chapter.
How to Analyse and Act Data
The main phase of the data science is the analysis phase. Analyze phase includes writing, executing and refining the programs to analyze the data and gather knowledge from it. These programs are known as data analysis scripts. Few scripting languages like Python, R and Perl are explained in brief under this section.
The iteration cycle of the analysis phase is explained with the help of a diagram for easy understanding. The analysis phase contains three main sources of slowdowns – Absolute running times, Incremental running times and Crashes from errors. These slowdowns are discussed in brief in this chapter.
This chapter also includes the challenges faced in the analysis stage of data science.
Reflection Phase and Dissemination phase
Reflection phase involves the communication of output about the analysis. The reflection can be in different forms like Notes, Holding meetings, Making comparisons and finding out alternatives. These reflection methods are explained in detail in this chapter. An example of the pictorial representation of the analysis is also given under this chapter for easy learning.
Dissemination phase is the final phase of Data science process. This is mostly in the form of written reports. The phase includes four stages – Write reports, Deploy online, Archive experiment and Share experiment. The major problem faced in this phase is the consolidation of the data. This chapter deals with the dissemination phase in detail.
Guiding Principles and Reasoning and Common Sense
The guiding principles of Data Science are not hard and fast rules to be strictly followed. But these guiding principles will help you in decision making. There are five guiding principles of data science explained in detail in this chapter. They are
- Be willing to fail
- Fail often and learn quickly
- Keep the goal in mind
- Dedication and focus lead to success
- Complicated does not equal better
Reason and common sense are foundations of data science. Without these two data is considered just as a collection of bits. Any context or model created by humans are not fully reliable. Blindly trusting such context will lead to wrong conclusions. Therefore you need to ask yourself few questions before carrying on with such context.
- What problem are you trying to solve?
- Does the approach make any sense?
- Does the answer make any sense?
- Is it a finding or a mistake?
- Does the analysis address the original intent?
- Is the story complete?
- Where would it lead you next?
Components of Data Science for Certified Analytics Professional Training
The components of a data science fall under different categories like
- Data types
- Execution models
- Learning models
- Analytic Classes
There is an interconnection among these components. These components are explained in detail in this chapter.
Classes of Analytic Techniques – Transforming, Learning and Predictive Certified Analytics Professional
The classes of analytic techniques are grouped into nine basic classes to help conceptualize the universe of possible analytic techniques. The nine analytic techniques are divided into three topics which are listed below
- Transforming – Aggregation, Enrichment, Processing
- Learning – Regression, Clustering, Classification, Recommendation
- Predictive – Simulation, Optimization
These techniques are explained in detail in this section.
Learning Models, Execution Models Scheduling and Sequencing
The learning models define the type of judgments and how the models change over time. The learning models are further classified into Learning style and Training Style.
- Learning style – Unsupervised, Semi-Supervised, Supervised
- Training Style – Offline, Reinforcement, Online
This tutorial contains the brief description of these learning models.
Execution model explains how data is manipulated to perform an analytic function. These execution models are further classified based on different dimensions and how they handle data. The classifications are
- Scheduling – Batch, Streaming
- Sequencing – Serial, Parallel
Each of the execution models of data science is briefed under this chapter.
Decomposing Analytical Problem
Decomposing the problem into several pieces is the first and foremost step in the analytic selection process. The concept of decomposition is explained using the Fractal Analytic Model. The Problem Decomposition Using the Fractal Analytic Model is explained using a pictorial representation. There are few things to consider when decomposing your problem
- Compound analytic goals that create natural segmentation
- Natural Orderings of Analytic goals
- Data types that dictate processing activities
- Requirements for human-in-the-loop feedback
- The need to combine multiple data sources
Data Science Maturity – Certified Analytics Professional
Calculating the maturity of a data science capability gives a different view to data science for Certified Analytics Professional within an organization. The data science maturity model describes the maturity progression and components that form data science capability. There are several stages of data science maturity model and at each stage, some knowledge is gained. There are five stages and each stage is explained by its description and example.
Feature Engineering Dimensionality Reduction and Model Validation
Feature Engineering is like a oxygen to data science. Feature engineering is the foundational skill and an challenging art in Data science. Feature engineering establishes the representation of data in the context of an analytic approach. The feature engineering concept is given in detail in this tutorial.
Machine learning gives an important result “Curse of Dimensionality”. The dimensionality reduction is needed under different situations. These situations are given under this chapter. Different types of dimensionality reduction techniques are also provided in this section.
Model validation helps in the construction of any model. This lets you know whether your hypothesis fits the observed data. There are many techniques available to combat model overfitting. One method is splitting the data into training, testing and validation sets. In this chapter, you will learn how the model is evaluated on each data set.
DATA CAP Questions
This chapter contains few interview questions on Data Certified Analytics Professional.
Certified Analytics Professional FAQ’s
- What level of programming experience is needed for this course?
- What are the resources needed for this course?
For this course, you need an internet connection and install the software in your system. The software includes Python, R, and SQL. You can also work with Hadoop but this course does not deal with Hadoop.
This is an excellent introductory course on Data science. It gives a good understanding of the basic concepts of the data science and provides real life examples of data science. The content of the course was well designed and the flow was also clear. I loved this course. It made me know about different model types and made me apply the data science concepts in real life projects.
Very interesting and engaging course. The course material is clearly and efficiently presented. The course not only covers the basic concepts but it also deals with some advanced topics in data science. It gives a good overview of data science projects and how to deal with the issues faced in data science. This online course is definitely the best course on data science. Highly recommended and all thanks to educba.
|Where do our learners come from?|
|Professionals from around the world have benefited from eduCBA’s Data Science for certified analytics Professional Training Courses. Some of the top places that our learners come from include New York, Dubai, San Francisco, Bay Area, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Chicago, UK, Hong Kong, Singapore, Australia, New Zealand, India, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many.|