#data_analytics

Benefits of Big  Data Analytics:

1. Strategic decision making for making forecasts and optimizing resources, such as supply chain optimization.

2. Helps in product development and innovation.

3. Personalized Customer Service to target customers to provide enhanced customer service and increase customer satisfaction.  Display products according to customers individual preference. Provide critical insights into customer behavior and market trends  and provide targeted promotions.

4. Risk Management to identify and mitigate risks.

5. Cost Reduction by optimizing the work force and save work and time.

6. Healthcare sector support by tracking patients health records of past ailments and  provide advanced diagnosis and treatment before it gets complicated..

7. Helps government and public sector to collect data through various sources such as AI Traffic Cameras, sensors, emails, CCTV, satellites, telephone calls, employee details, Customer details, Electric bills, and more and provide quality services like preventing crime, improve traffic management, and predicting natural disasters, optimize supply chain processes, reduce cost, improve product quality through predictive analysis, improve teaching methods through adaptive learning etc.

8. Helps Banking sectors track and monitor illegal money laundering and theft.

9. Boost customer acquisition and retention and maximize sales using Big Data

10. Scalability

Challenges in Big Data Analytics:

1. Keeping data secure ie., handle data privacy and security concerns as they arise

2. Finding the right tool and platform according to your business needs.

3. Maintaining Quality Data

4. Sharing and Accessing Data

5. Analytical and Technical challenges

Ready to dive into a career in Data Analytics but do not know where to begin? Do not worry. EJ Academy is at your service. Do call us and we will give you brief introduction to Data Analytics, the role of a Data Analyst, skills and responsibilities of the Data Analyst, the tools used for garnering insights, understanding the fundamentals of Data Analytics process which includes data gathering, cleaning, analyzing and interpreting data, dashboard and data visualization using tools like Power BI or Tableau, deriving insights for better decision making etc., the topics we cover in our course, the projects, learn and understand topics with various case studies for better understanding etc., We have data experts in our team who will assist you in sharing their tips and advise to start Data Analytics as a new career. This course will also help you to differentiate between the roles of Data Analysts, Data Scientists, and Data Engineers.

Data Analytics is the process of analyzing raw data, uncover patterns, hidden trends, exploring new correlations, and derive a conclusion with valuable insights from that data that can help businesses make better decisions to make predictions. Business around the world generate huge amount of data in the form of customer data, transactional data, social media posts, customer reviews, log files, web servers etc. Companies are required to make use of formed data to create a value out of it in order to make effective business decisions.

How to use Data Analytics in your businesses: 1) Use automation for repetitive tasks. 2) Customize your Customer service according to your needs. it helps targeting your customers better. 3) Facilitate effective decision making for making predictions 4) Effective marketing strategy through valuable insights. Make shopping experience more personalized, give relevant product suggestions, optimize product assortments etc., 5) Provide tools for Data Visualization such as Power BI or Tableau, such that the conclusions are understood even by non technical people.

Devising risk mitigation strategies with tools available by anticipating potential risks

Some sectors where Data Analytics is used: Almost all the sectors use Data Analytics in their businesses. A few sectors include:

Banking

Healthcare

Education and Research

Retail

Manufacturing

Logistics

Transport

Search Engines

Communication media 10.Energy Sector

By the end of our course you will have a fairly good idea of the real-world scenario of data analysis tasks.

Showcase your skills in Data Analytics by joining our comprehensive three months program, by learning all the tools, softwares, libraries, and concepts taught in a case based manner, and solve real-world problems.

We go to industries, understand their requirements and find out what they want to cover in our program, design case studies inspired by the discussions we had with our industry partners, and solve a few real world problems. For very problem, we pick up a most challenging dataset, and try to look for insights in them. Most people make a mistake of gathering data from somewhere, run some code and generate a wonderful graph from it and they are done with it. No. your job is to get the most valuable insights from the data and try to pick up slightly challenging problems and try to apply them on real world problems and that's how you build real world skills required for Data Analytics.

Basically, we will be covering the 6 phases of Data Analytics life cycle in our training programme.

What are the goals and objectives

Understanding data and the process of acquiring data.

Importance of Data Cleaning and Data Transformation

Methods of enhancing Data

Data Analytics

Data Visualization

For doing the above you need to acquire two important skillset:

a) SQL (for extracting and manipulating data)

b) Power BI (for data analytics & visualization)

Convey your data with visualizations on dashboard and reports using Power BI. We build a series of portfolios from the basic to state-of-the-art deep learning models.

JOIN OUR DATA ANALYTICS TRAINING PROGRAM AT EDUJOURNAL TODAY.

Data Science has become a backbone for many industrial organizations today.  Many students are pursuing a career in Data Analytics and Data Science to hone their skills, to take advantage of the numerous opportunities existing in the markets..   With Data Science,  we use the raw data set to gain insight, or understand the trend or projections to enable management to make data-driven decisions using various statistical, mathematical and computational models and tools.  Therefore, to make a career in Data Science you will need a basic mathematical and Statistical foundation, computing skills, critical problem solving skills, strong analytical and presentation skills, and creativity.  The role of a Data Scientist could range from saving operating cost of the company, to mining data to provide valuable insight for better decision making, to improving customer experience while using websites or app etc

Python is one of the most versatile languages out there today capable of building machine learning applications with the ability to make predictions with the data mined.  For Data Analytics, we have two main libraries, which are the workhorses  of python called Pandas and Numpy, which can mimic many built-in-features of R Language, and we have Matplotlib provided for creating Visualization and SciKit-learn for machine learning with in-built algorithms and models for classification, regression, clustering, dimensionality reduction, model selection,  pre-processing.  For statistical inference models you have statsmodels.  For Deep Learning you can choose Keras, TensorFlow or PyTorch. On the other hand,  R language also have some useful packages for Data Science  like ‘tidyverse’ which is a collection of useful packages like dplyr for data manipulation, tidyr for data cleaning, and packages like purr and tibble for built-in-functionalities of R.    Data is driving major industrial sectors today, and the demand for data scientists are only growing day by day.  You gain skills by doing more and more projects.  So be patient and focus on the task in hand. 

A good Data Science program ensures that the student receives the right amount of practical and theoretical knowledge and skills to face real-world challenges.  We at eduJournal (www.edujournal.com),  make Data Science professionals industry-ready by providing them training and industry-driven projects by catering to the needs of industries.  If you are interested, please do contact eduJournal for more details.

The following are considered essential, if you intend to make a career in data science.

1. Develop a basic knowledge and understanding in Statistical, Mathematical and Computing concepts: Data Scientists are professional who turn data into insights or tends or help making predictions and recommendations. Having the knowledge of various concepts, tools, libraries and mathematical models are central to applying them.

2. Develop communication and presentation skills: Your analysis need to be communicated to the management. A good data scientist can contextualize and interpret the solutions to the stakeholders of varying backgrounds through various forms of communication viz., written communication (ie., in the form of report and Summary), visual communication (ie., clear and intuitive plots, analytics and visualization), and spoken communication (ie., presentation, iterative design, project specifications etc).

3. Identify promising opportunities that align with your skill and intellectual ability: We all have individual skills and intellectual ability, which we would love to develop and nourish. Make an assessment of how well your aspirations and goals align with the critical project path of the company or the environment you are in.  Select the projects of companies whose critical path best aligns with your skill and ability. For eg., if you have a strong data-driven skills for developing models using machine learning, then join a team or project that best aligns with your skills that enables you to understanding the workflow of machine learning, building complex pipelines with python and solving concrete business problems, which will ultimately enable you to develop your career in machine learning.  There is no point in applying for a data scientist’s job that focuses on experimental design and product analytics when your interest is somewhere else. Do not join a job just because it is popular, lucrative and in-demand.  You need to take into account your skill, knowledge and ability to perform in that job well.  Also, try to gain practical exposure through internships.  Keep yourself updated with the latest trends in industry viz global activities is your domain, technological advancements, best practices etc

4. Upskill/Reskill yourself with programming languages, libraries, and tools: A very common questions among Data Scientists on which programming language to choose. “Instead of thinking about which programming language to learn, think about which language offers you the right set of Domain Specific Languages(DSL) that fit your problem. – Reditt”.  In other words, it’s not about whether I should learn Python, but it’s whether Python provides me with the right library and tools to get my job done. Let’s take another instance of R Language and Python.  If you are developing statistical models then R is the ideal choice. Research scholars develop statistical method for developing newer R packages.  On the other hand, Python is best for building production data pipelines for machine learning. Instead of depending on a specific library or tools, or a program language, identify the best set of resources that will enable you to solve a particular problem. There are other languages as well to support Data Science like Matlab and Julia but the most prominent are Python and R languages

5. Learn from experts before you take a leap: Choose a training program such as Master Program for Data Analytics and Data Science from eduJournal , that strengthens your skills and gives you hands-on-experience while you work on projects and deal with continuous practice, case studies and assessments.  You will get an industry recognized certificate after completion of the course to validate your skill in Data Science, along with assured placement assistance, and  insightful sessions with industry experts. Also, we would assist you to build a digitally professional portfolio that can be easily shared across with recruiters showing your proficiency in Data Science.

Getting placed in a Data Science sector will never come easily.  You need to put in enormous hardwork and attend numerous interviews to be successful.   The key to becoming a successful Data Scientist is to gain the required knowledge and skills in Data Science, and the best way to achieve it is by working on some real-world  projects.  Keep in mind the points mentioned above before placing your feet into your new job. For a career in data science, it may serve you well if you are able to represent data science on a venn diagram as a confluence of statistics, programming and domain knowledge.  Despite each occupying a proportionate share in the intersecting area, some may warrant a higher weightage than the others. 

Data Science has a curious distinction of being one of the few fields of study that leaves a practitioner without a domain. For instance, we have demand for professionals who can build financial analytics programs to foresee two main objectives viz., to predict profit and the other to protect the bank assets.  Data Science has the innate ability to mine large data (data mining) and uncover profitable insights through pattern recognition which is an invaluable skill. Also, Data Science can help banks minimize credit risk by uncovering risky behavioral patterns of individual owners and institutional borrowers.

Opting for a career in Data Science is a lucrative and in demand job across multiple domains like healthcare, banking, retail, education, government, transportation, media and communication etc.,   Therefore, taking sufficient time to understand the core concepts will not only help you during the interview process, but will also help you to understand whether you are truly interested in the Data Science career. 

Looking to get a career in the field of Data Science? YES. it is a lucrative, in-demand, progressive, futuristic and growth-oriented technology. So what is it that makes data science such a scorching hot field to get into?

Being a Data Scientist involves having a basic skill set viz., knowledge of basic mathematical, basic statistics and probability, basic computational and business analytical skills. In other words, as a data scientist, you need to consistently have one foot on the IT sector, and the other planted firmly in the business world. You need to have expertise in all the domains.

Data Science is mainly focused on exploration of data , making an inference from the data, and deriving an insight or prediction from the inference with the help of various statistical and mathematical models, programming languages like python or R language, algorithms in machine learning with python, visualization tools like Tableau or Power BI etc., Data Science requires the usage of both structured and unstructured data.

The machine learning requires two inputs for it to operate viz.,

a) Algorithms and

b) Data.

You should always provide clean data, otherwise the models that you develop will be all junk. You can derive insights and trends from data using any of the models and tools mentioned above. The choice is yours and the decision is taken after taking in account the complexity and scale of the problem. Thus, it helps business in taking the right decisions at the right time and also facilitates better strategic planning.

Let’s take an example in the healthcare sector where to detect or identify cancer in a person early, various medical reports (data) of the patient are provided to the system. The algorithms in machine learning makes learning by using the algorithms and comparing your data with previous available records of patients, comparing the various parameters with the existing normal values, making an analysis and derives at a final conclusion (result). The more data (historical data) of patients you have, the more accurate your result will be. Also, if you provide some arbitrary historical data, your result will not reflect the correct picture.

If you wish to make a career in data science, you have two options viz.,

Research Field like PhD and Post Doctoral: If you intend to go into the research field, you need to be qualified in that area of study, and have a thorough knowledge and understanding of mathematical, statistical and computational concepts related to the study.

Product Analytics and Visualization for industries and Service Sectors: To choosing this program, you need to have a basic knowledge of the mathematical and statistical concepts, basic probability, basic knowledge of python and a good knowledge of python libraries like pandas, matplotlib and numpy and various tools associated with it viz., visualization tools like Tableau or Power BI. Also, be well versed with SQL databases (MySQL, SQL Server or Oracle), and Business Analytics along with machine learning and Deep Learning (including neural networks), Predictive Modeling,  and NLP.  Our master program in Data Science is basically dealing with developing Product Analytics and Visualization for companies and our training program covers all of the above.

Our Data Science master program at eduJournal (www.edujournal.com), is a comprehensive program, designed to help learners of all skill levels, master this technology. Our syllabus is designed in such a way that it covers everything from the basic to advanced concepts which include expert instructions, coding exercise, quizzes, case studies and real world projects. It provides learners with the skill and knowledge to analyze, visualize and derive insights, trends or predictions from the data and hone their skills by learning concepts by providing case studies associated with it and working on real world projects. We also hone your skills with Data Science Interview Questions widely asked in interviews like scenario based interview questions, where you will be given a scenario and asked questions based on that scenario. To get through this round you will need a good working practical knowledge, which can be achieved by doing some real world projects. We will guide you to prepare for that round. Also, we have Data Science quizzes to measure your Data Science skills.

Roles and Responsibilities of a Data Scientist:

1. Understanding the Clients requirements.

2. Gather and Extract the dataset associated with the requirement.

3. Clean and pre-process the data.

4. Explore, Analyze and visualize data using various analytical tools and various statistical or mathematical models and computational libraries and algorithms.

5. Derive insights and make predictions.

6. Evaluate the performance of these models and make improvements if required.

7. Communicate the results and findings to stakeholders (client).

8. Monitor and maintain the performance over time.

How to become a Data Scientist:

1.Learn the basics of python (viz., libraries like pandas, matplotlib, numpy, scikit-learn, TensorFlow etc., and developing algorithms for machine learning using python) or R Language (if you are developing statistical and mathematical models).

2. Familiarize yourself with the tools used for data analysis like the Power BI, Seaborn and Tableau and the various libraries mentioned above.

3. Understand the basic mathematical concepts (linear algebra, decision trees), statistical concepts (Linear regression) and probability, neural networks (Deep Learning & AI), which are required for developing algorithms which are the core to data science.

4. Familiarize yourself with working with different types of data such as structured and unstructured data and various file formats like json, csv, xls, sql dump .

5. Understand the importance of data ethics and how to handle sensitive data carefully.

Advantages of Data Science:

1. Abundance of opportunities: Data Science is greatly in demand today, and there is lots of opportunities for job seekers with excellent remuneration packages. It is estimated to generate 11.5 million jobs by the year 2026. As the data becomes increasingly important to aid in decision making process, the demand for data scientists continues to grow, making it a highly demanding skill in the job market.

2. Used in multiple domains: it is a versatile field used in multiple domains such as finance, healthcare, marketing, banking, insurance, telecommunication, automobile, consultancy services etc., giving you the flexibility in career path.

3. Empowering managements to make better decisions: Enables companies to make smart business decisions thus, improving the overall performance of the company. The ability to work with large quantity of data and generate insights or predictions, creating new patterns, analyze data and generate reports etc., can help in the overall development and increase the productivity of the company.

4. Provide personalized insights: Enables computers to understand and predict human behavior and make data-driven decisions based on historical data. For eg., Ecommerce sites providing personal insights to users based on past historical purchases.

5. Handling complex problems: Facilitates breaking a larger complex problem into smaller manageable units and deriving at a solution.

6. Technological Advancement: With the improvements in technology, the ability to collect and store data, make analysis from data, deriving insights and make predictions etc., has made data science a popular field with greater potential for innovation.

7. Personal growth: It is a rewarding career for professionals who wish to use their problem solving skills and creativity to find solutions to problems.

Disadvantages of Data Science:

1. Mastering Data Science is close to impossible: Data Science is a vast subject. The role of a data scientist depends on domain in which the company is specialized in. For example, in a healthcare sector, a data scientist working on the analysis of genome sequencing will require some knowledge of genetics and molecular biology to create an algorithm for machine learning.

2. Arbitrary data may yield unexpected results: Many times, the data provided is arbitrary and does not yield desired results.

3. Data Privacy issues: While data scientists help clients make data-driven decisions, the ethical concern of individuals regarding the preservation of data privacy and its usage have been a cause for concern.

Some common Python libraries used in Data Science for data analysis:

a) pandas

b) numpy

c) matplotlib

d) TensorFlow

e) Scipy

f) keras

g) scikit-learn

Data Science has become an inevitable part of any industry today. The role of a data Scientist is to assist the management to make better decisions. Data Science is a trending field today, helps you develop valuable skills, opening up newer career opportunities and has a great impact on society at large by offering both personal and professional growth. Our program provides students with real world projects which strength their portfolios to get their dream Data Science job by implementing these real-world Data Science projects.

Dive into the world of endless possibilities as you learn to harness the power of data to uncover hidden insights, from predicting trends to uncovering patterns, Data Science has the power to transform the way we live and work. Whether you are an absolute beginner or an experiences professional hoping to switch over to a Data Science career, our master program will take care of your journey to explore the world of data analytics and visualization. Get ready to uncover the future with Data Science!!!