Using Analytics To Empower Decisions
Accelerate Your Upskilling Journey
- This program aims to provide hands-on expertise in using tools and methods to make important business decisions across an organization.
- Each framework will be illustrated with examples and participants will be engaged in problems solving applications. The training and discussion will cover multiple industries, including apparel, e-commerce, financial services, health care, media, entertainment, and technology and retail.
- This provides a holistic learning journey and will prepare you to implement business analytics at your place.
- CAIA engages with your organization to enable your employees upskill themselves through our Executive Program or Managerial Program.
- Understand and interpret the data from your own organization to improve your business processes and function optimally.
Words Inspiring To Do Better
“I did my graduation from Delhi University in Economics. Following which I joined Madras School of Economics where I pioneered in creating different articles related to data modeling and finance marketing. I felt learning the theoretical concepts to solve real-life problems did not meet the requirement of a business solution.Madhur RajAnalyst, Citibank International
Here, my learning crystallized into combining the theoretical concepts and practical hands-on experience needed to meet the requirement of the industry. A very sharp learning curve augmented with high-quality study material and 24*7 support through the ‘Learning Management Portal’ ensured we understood and implemented what we learned.”
“Being a graduate in BCA and a fresher, I received complete support from the faculty at CAIA who taught us from scratch, in a very effective manner. We were given exposure to real-time datasets and complex business problems. Our ability to analyze and think out-of-the-box developed in a very short time. The team at CAIA took a genuine interest in our career development. Finally, I am happy that I chose CAIA over other institutes and invested my time and money wisely.”Swetha DamotharanData Analyst, Systech Solutions
“The full stack Program in Artificial Intelligence and Advance analytics at CAIA provides in-depth understanding of Analytics with hands-on experience. This intensive program makes us work on data sets from multiple domains. The best part of this course is that it includes step-by-step assignments which ensure that the concept is understood. Today, I have been able to successfully steer my career from Direct Sales in the Banking Sector to being a Data Analyst within a span of just 3 months.”Mahesh Dinkar WathareData Analyst, Systech Solutions
“The training provided by CAIA was an eye-opener for me to the field of Analytics. On one hand, the Data Management vertical taught me how to identify and establish relationships between the data and its corresponding business process. And on the other hand, the advanced analytics module helped me with descriptive and predictive analytics.Krithika LaxmananData Analyst, SCIO Health Analytics
My passion for Machine Learning grew to a great extent as my trainers unleashed the effectiveness of a data model to us when applied to the real world business. The project presentations helped me in evaluating my strength and weaknesses, where I had to take on the role of Business Consultants. On an ending note, the learning obtained from CAIA was extremely helpful in resuming my career back. I sincerely thank CAIA and the entire team.”
Data Science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization’s competitive advantage. Data Science incorporates tools from multi-disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the Data Science field include mining, statistics, machine learning, analytics, and some programming.
Data Mining is a process by which companies extract useful information from raw data (data may be in any form i.e. structured, unstructured or semi-structured). By using one or more software, from huge sets of data, patterns are discovered that help to learn about customers and develop effective marketing strategies. This term was most widely used in the late ’90s and early ’00s when a business consolidated all of its data into an Enterprise Data Warehouse. All of that data was brought together to discover previously unknown trends, anomalies, and correlations.
Data Analysis is a process to inspect, clean and transform data to extract the useful information that is required using analytical and logical reasoning. There are many methods to analyze data. The analysis is really a heuristic activity, where scanning through all the data the analyst gains some insight. It is about applying a mechanical or algorithmic process to derive the insights, for example, running through various data sets looking for meaningful correlations between them. These methods include data mining, text analytics, business intelligence etc.
Data Science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Data Science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines.
Some of the highlighting skills that a data scientist should possess, are as described below.
Statistical Skills: A basic statistical skill set is required to be a data scientist, e.g. the ability to summarize data, create statistical graphs, perform basic calculations etc, is necessary. Statistics is required to know the basic characteristics of data
Computer Skills: Data is complex, and with the concept of big data, computers skills such as knowledge of software such as Python, R, SAS, Hadoop or at least few of these is necessary to have in order to become a data scientist.
Problem Solving Skills: This is an essential generally for all jobs, but for data analysis it is important because data can be analyzed in a lot of different ways, and in order to solve the problem at hand or predict future problems and their solutions based on the data, it is important for a person to adopt a holistic approach in identifying problems and solving them on the basis of data. Therefore, problem-solving skills, i.e. defining the problem accurately, suggesting solutions to the problem, and providing factual evidence in the form of data to support the solutions is necessary. These skills can be acquired with the knowledge of Data Mining, Machine learning, Text analytics, Deep learning and many more of such approaches.
Target Industry Knowledge: It is not only important to know how you can explain your data differently to different people in your company, but it is also important to have the knowledge of your client’s industry, in order to analyze and present data effectively, and actually, enable your problem-solving skills in that industry.
Communication Skills: In a company, a project manager might view data differently from a CEO, whereas project manager might just be focused on data analysis of a certain project, the CEO will be looking at how the data of this project could affect other projects of the company. Therefore, for data-analysis, a person should have strong analytical, communication and presentation skills to present the data accurately to different facets of a same organization, and even to external partners and clients.
Step 1: Learning the basics for python- Python is easy to start language. So as a novice first you need to understand all the basics for the language.
Step 2: Basic Statistics & Mathematics- Would highly recommend learning statistics with a heavy focus on coding up examples, preferably in Python or R.
Step 3: Python for Data Analysis- Once you are done with Step 1 & Step 2 then it’s time to get hands-on experience with some real data analysis programming, Learn to install Anaconda, Jupyter notebook, Python packages like Numpy, Pandas etc.
Step 4: Machine Learning- It is classified into the following two categories:
(i) Supervised learning (Regression, classification, support vector machines, kernels, neural networks).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, Install Python Scikit Learn Library for practicing Machine Learning in Jupyter Notebook
Step 5: Learn more related skills like NLP, Deep Learning, Big data technologies, Data visualization, etc. Use Python-based libraries like Nltk, Keras, Tensorflow to learn implementation.
Step 6: Practice – Try to get exposure to data through hands-on projects, assignments, internships. Do as many data analysis competitions, Data Hackathons or related competitions which give exposure to data and real-world problems as you can.
This is only a rough pathway- you can change the sequence as per your need.
According to HakerRank Developer Skills Survey 2018, by 2020, all alone in the USA the jobs openings for data professionals will increase by 364,000 openings to 2,720,000 according to IBM. It is just insight from opening for jobs. Future Scope of Data Science is high and it is going to stay here for a while. Apart from that, Data Scientist tops the list of ‘Best jobs in the USA’ in an annual survey conducted by Glassdoor, an online portal for job hunting, for consecutive 3 years. 3 out of the 5 highest paying professionals are related to Data Science! Hence if the only salary is your concern, Data Science is the right path for you. In India, salaries vary from 0-3 lakhs to 1 crore plus, all based on your skills and experience.
The goal of the statistical analysis is to summarize the data. Statistical methods make tight assumptions about the problem and data distributions. Generalization of conclusions is pursued using statistical tests on the training dataset. It promotes data reduction as much as possible before modeling (sampling, fewer inputs), that is often easy to work with small data sets.
The goal of data science is to learn from data of all kinds. Data science techniques do not make any rigid pre-assumptions about the problem and data distributions in general. Generalization of conclusions is pursued empirically through training, validation and test dataset. Redundancy in features (variables) is okay and often helpful. It is preferable to use algorithms designed to handle a large number of features. It does not promote data reduction prior to learning. It promotes a culture of abundance: “the more data, the better it is”. Data science techniques are capable of solving complex problems.
In a nutshell, Python is better 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. The IEEE Spectrum ranking is a metrics that quantify the popularity of a programming language. In 2017, Python made it at the first place compared to a third rank a year before. R is in 6th place. Features of Python like easy to learn, strong support for analytics through packages and adaptability make it most used language for analysis in the domain of data science.
A recommender system is a subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.
If an algorithm learns something from the training data so that the knowledge can be applied to the test data, then it is referred to as Supervised Learning. Classification is an example for Supervised Learning. If the algorithm does not learn anything beforehand because there is no response variable or any training data, then it is referred to as Unsupervised Learning. Clustering is an example of Unsupervised Learning.
Deep Learning is a model of machine learning which has shown incredible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the human brain. The superiority of the human brain is an evident fact, and it is considered to be the most versatile and efficient self-learning model that has ever been created.