Data Science Components: Machine Learning

Data Science Components: Machine Learning
Machine Learning involves the use of algorithms and mathematical simulations to train machines and enable them to respond to developments in the world around them. Time series forecasting, for example, is widely used in trading and financial processes these days. The computer will forecast the results for the coming months or years based on past data trends. This is an example of machine learning in action. Humans generate a massive amount of data every day in the form of clicks, requests, videos, photos, tweets, posts, RSS feeds, and so on.

Data Science Components: Big Data and Business Intelligence

Big Data is a term used to describe large amounts of unstructured data. Big Data technologies and methods specifically assist in the translation of unstructured data into structured data. Consider someone who needs to keep track of the pricing of various goods on e-commerce pages. Using Online APIs and RSS Feeds, he can get data on the same brands from various websites. Then arrange them in a formal manner.
Business Intelligence: Any business has and generates an excessive amount of data on a daily basis. When closely analysed and viewed in visual presentations of diagrams, this evidence will help people make better decisions. This will assist managers in making the right judgement possible by thoroughly delving into the themes and information brought to light by the studies.

Data Science Required Qualifications : Tools: R knowledge

To become a data scientist, you'll need the following skills:
R knowledge: R is used for data processing, as a programming language, as a mathematical analysis environment, and for data visualisation.

Data Science Required Qualifications : Tools: Python coding

Python coding: Python is widely used to apply mathematical structures and principles since it has a large number of libraries and packages that can be used to create and execute models.

Data Science Required Qualifications : Tools: MS Excel

MS Excel: For all data entry work, Microsoft Excel is considered a must-have. It comes in handy when dealing with vast quantities of data and applying formulae, calculations, and diagrams.

Data Science Required Qualifications : Tools: Hadoop

Hadoop Platform is a distributed computing system that is open source. It's used to keep track of how large data programmes are processed and stored.

Data Science Required Qualifications : Tools: SQL database

SQL database/coding is mostly used for dataset preparation and extraction. It can also be used to solve issues such as graph and network analysis, search behaviour, fraud detection, and so on.

Data Science Required Qualifications : Tools: Technology

With so much unstructured data available, it's important to know how to use it. This can be accomplished in a multitude of ways, including by APIs and web servers.

Data Science Required Qualifications : Methodology

Data scientists also focus on machine learning algorithms like regression and classification. Clustering, time series, and other methods that are based on mathematical algorithms necessitate a high degree of mathematical understanding. Working with unstructured data: Since much of the data generated every day is unstructured, such as photographs, remarks, tweets, search history, and so on, learning how to turn this unstructured data into a standardised form and then working with it is a very useful ability in today's market.

Data Science Required Qualifications: Understanding of Business

Business Acumen: Analytics professionals work their way up the corporate ladder from mid-management to high-management. As a result, gaining market experience is a must for them.

Module 1: Python

Python is the most important and necessary topic that every data scientist should have knowledge about. In this section, our instructors will take you through the basics of Python and areas where it can be used. You will learn how to use some of the current tools such as Numpy, Pandas, and Matplotlib. Therefore, module 1 includes –
Environment set-up
Jupyter overview
Python Numpy
Python Pandas
Python Matplotlib

Module 2: R

Used for statistical and data analysis, R programming language is one of the advanced statistical languages used in data science. This module teaches you how to explore data sets using R. Here you will learn –
An introduction to R
Data structures in R
Data visualization with R
Data analysis with R

Module 3: Statistics

When working with data, the knowledge of statistics is necessary and an important skill set that you must have. In this module, you will learn –
Important statistical concepts used in data science
Difference between population and sample
Types of variables
Measures of central tendency
Measures of variability
Coefficient of variance
Skewness and Kurtosis

Module 4: Inferential statistics

Inferential statistics is used to make generalizations of populations, from which samples are drawn. This is a new branch of statistics, which helps you learn to analyze representative samples of large data sets. In this module, you will learn –
Normal distribution
Test hypotheses
Central limit theorem
Confidence interval
T-test
Type I and II errors
Student’s T distribution

Module 5: Regression and Anova

This lesson will help you understand how to establish a relationship between two or more objects. ANOVA or analysis of variance is used to analyze the differences among sample sets. Here you will learn –
Regression
ANOVA
R square
Correlation and causation

Module 6: Exploratory data analysis

In this lesson you will learn –
Data visualization
Missing value analysis
The correction matrix
Outlier detection analysis

Module 7: Supervised machine learning

This is a comprehensive module to help you understand how to make machines or computers interpret human language. You will learn –
Python Scikit tool
Neural networks
Support vector machine
Logistic and linear regression
Decision tree classifier

Module 8: Tableau

Tableau is a sophisticated business intelligence tool used for data visualization. In this lesson, you will learn –
Working with Tableau
Deep diving with data and connection
Creating charts
Mapping data in Tableau
Dashboards and stories

Fundamentals of Data Science

Introduction to Big Data, Data Science, and Predictive Analytics
• Introduction to Azure ML Studio
• Fundamentals of Data Mining
• Introduction to R Programming

Fundamentals of Data Science

Data Exploration, Visualization, and Feature Engineering
• Hands-On Labs: Data Exploration, Visualization, and Feature Engineering
• Machine Learning Fundamentals

Classification Algorithms

• Introduction to Predictive Modeling
• Decision Tree Learning
• Logistic Regression
• Naïve Bayes
• Hands-On Lab: Building a Classifier
• Hands-On Activity: Determining the best split for

Classification Models, Evaluation and Cross Validation Regression Algorithms

• Linear Regression
• Regularized Regression Models
• Hands-On Lab: Building a Regression Model
• Hands-On Activity: Evaluating Performance, Finding
Maxima and Minima, Gradient Descent, Visualizing
Features and Parameters

Unsupervised Learning

• K-Means Clustering
• Hands-On Lab: Using K-Means Clustering
• Text Analytics
• Content-Based and Collaborative Filtering
• Evaluation of Recommendation Systems. DCG, nDCG
• Hands-On Lab: Analyzing a Document Collection
• Hands-On Activity: Using TF-IDF and Cosine Similarity
to Query a Document Collection

Recommender Systems

• Bootstrapping, Bagging, and Boosting
• AdaBoost
• Random Forests
• Hands-On Lab: Building a Random Forest Classifier
• Hands-On Activity: Calculating Probabilities with
Binomial Distribution, Sampling with and without Replacement Ensemble Methods

Operationalizing Machine Learning Models

• Metrics and Methods for Evaluating Classification and Regression Models
• Tuning Machine Learning Algorithm Parameters
• Hands-On Lab: Building a Classification Model in Azure
ML Studio • Hands-On Lab: Deploying a Predictive Model as a Service

Fundamentals of Big Data Engineering

• Introduction to Large-Scale Online Systems • Hive Tutorial • Hands-On Labs: Creating a Hadoop Cluster and Writing Hive Queries

Handling Real-Time and Streaming Data

• Message Queues and Real-time Analytics
• Hands-On Lab: Creating a Streaming Analytics Pipeline

Data Science Essentials

• Introduction to Online Experimentation and A/B Testing
• Hands-On Activity: Performing a t-Test

Efficient graph processing at scale

One field that needs efficient graph processing is social media analytics. The role of graph databases in big data analytics is covered extensively in research. Working on efficient graph processing on a large scale remains a fascinating problem. The research problems to handle noise and uncertainty in the data:- Identify fake news in near real-time: Fake news spreads like a virus in a bursty manner, so dealing with it in real-time and at scale is a pressing problem. The data may come from Twitter or fake URLs or WhatsApp. It could appear to be an authenticated source, but it may also be a fake, making the problem more challenging to solve.

Machine Translation to Local Languages Using Neural Networks

One can use Google translation for neural machine translation (NMT) activities. However, with government funding, there is a lot of research being done in local universities to do neural machine translation in local languages. The latest advances in Bidirectional Encoder Representations from Transformers (BERT) are changing the way of solving these problems. It is possible to join those projects in order to solve real-world problems. At a large scale, effective graph processing: Social media analytics is one field where efficient graph processing is needed. In the research work, the role of graph databases in big data analytics is extensively discussed. Working on efficient graph processing on a large scale remains a fascinating problem.

Detect fake news in near real-time

The following are the issues that need to be addressed in order to deal with data noise and uncertainty: - Detect fake news in near real-time: Managing fake news in real-time and at scale is a pressing issue, as fake news spreads like a virus in a bursty manner. The details could have come from Twitter, fake URLs, or WhatsApp. It may appear to be an authenticated source, but it could also be a fake, making the problem more difficult to solve. Google Translation for Neural Machine Translation (NMT) to Local Languages: For neural machine translation (NMT) operations, Google Translation can be used. However, with government funding, there is a lot of research being done in local universities to do neural machine translation in local languages. Bidirectional Encoder Representations from Transformers (BERT) is a recent development that is revolutionising how these problems are solved. You will participate in such programmes to help solve real-world problems.

Data scientists work closely with business associates to identify their priorities and how data can assist them in achieving them. They create algorithms and statistical models to extract the data the organisation needs, as well as assist in data interpretation and peer exchange. Despite the fact that each project is unique, here is a general overview of the data collection and analysis process:

1. Start the exploration process by asking the right questions.
2. Gather information
3. Cleanse and process the data
4. Compile and save data
5. Data investigation and exploratory data analysis are the first steps in the data analysis process.
6. Choose one or more possible models and algorithms to work with.
7. Use data science techniques like machine learning, mathematical modelling, and artificial intelligence to solve problems.
8. Evaluate and develop outcomes
9. Inform stakeholders about the final outcome.
10. Make changes in response to feedback
11. Use the same steps to solve a new problem.

The most common careers in data science include the following roles.

1. Data scientists: Design data modeling processes to create algorithms and predictive models and perform custom analysis
2. Data analysts: Manipulate large data sets and use them to identify trends and reach meaningful conclusions to inform strategic business decisions
3. Data engineers: Clean, aggregate, and organize data from disparate sources and transfer it to data warehouses.
4. Business intelligence specialists: Identify trends in data sets
5. Data architects: Design, create, and manage an organization’s data architecture

E-commerce
E-commerce and retail are some of the most important sectors that need data processing at the largest stage. The successful implementation of data analysis would allow the e-commerce organisations to forecast the sales, income, losses and even trick consumers into purchasing products by monitoring their behaviour. Retail brands analyse customer profiles and based on the findings, they market the related goods to drive the customer into buying.

Manufacturing

Data Science is used in manufacturing for a number of purposes. The main use of data science in manufacturing is to impact efficiency, minimise risk, and increase benefit. Following are the few fields where Data Science can be used to enhance efficiency, processes and forecast the trends

Performance, quality assurance, and defect tracking

Predictive and conditional maintenance Demand and throughput forecasting Links with suppliers and the supply chain Pricing on the global market Designing new facilities and automating existing ones For product development and production techniques, new processes and materials are being developed. Greater energy production and sustainability.

Finance & Banking

The banking sector has been rapidly changing since the financial crisis of 2008. Banks were among the first to use information technology to enhance their processes and security. Banks are using technology to better understand their clients, maintain them, and attract new ones. Financial firms are using data mining to better understand their customers' transactional behaviours, enabling them to communicate with them more meaningfully. The transaction data that banks have access to is used in risk and fraud management. The implementation of data science has resulted in better control of each client's personal data. Banks are starting to realise the value of collecting and analysing not only debit and credit transactions, but also purchase histories and trends, modes of communication, Internet banking data, social media, and mobile phone use.

Health-care services

Every day, electronic medical records, billing, clinical systems, data from wearables, and other sources generate massive amounts of data. This provides a significant opportunity for healthcare providers to improve patient care by using actionable lessons from past patient records. Of course, data science is the driving force behind it. The healthcare industry is being revolutionised by data scientists all over the world. They're working to optimise every aspect of healthcare operations by unlocking the potential of data, from optimising care quality to achieving operational experience.

Salary packages of data scientists depend on their qualifications, job roles, job profiles, and years of experience. The annual package of a fresher usually lies between 6 lakh to 8 lakh per annum (as per Payscale.com). The table below consists of the average salary packages offered to different job roles of a Machine Learning Professional (as per Payscale.com).

Job Profile (Avg Annual Package (In Rupees))

1. Data Analyst: 1,97,000 - 9,12,000
2. Data Scientist: 3,37,000 - 20,00,000
3. Software Developer : 2,06,000 – 10,00,000
4. Sr. Software Engineer/Developer/Programmer: 4,13,000 - 20,00,000
5. Senior Business Analyst: 4,29,000 – 20,00,000
6. Business Analyst, IT: 2,86,000 – 10,00,000
7. Senior Data Analyst: 3,10,000 - 10,00,000
8. Software Engineer/Developer/Programmer: 2,32,000 – 10,00,000

Below is the list of some renowned companies that recruit data scientists

Job Profile (Avg Annual Package (In Rupees))

1. Amazon
2. LinkedIn
3. IBM
4. Walmart Labs
5. Busigence Technologies
6. Fractal Analytics
7. Sigmoid
8. Flipkart
9. Mate Labs
10. Couture
11. and many more

Most data scientists use the following core skills in their daily work

1. Statistical analysis: Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection.
2. Machine learning: Implement algorithms and statistical models to enable a computer to automatically learn from data.
3. Computer science: Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.
4. Programming: Write computer programs and analyze large datasets to uncover answers to complex problems. Data scientists need to be comfortable writing code working in a variety of languages such as Java, R, Python, and SQL.
5. Data storytelling: Communicate actionable insights using data, often for a non-technical audience.
6. Data scientists play a key role in helping organizations make sound decisions. As such, they need “soft skills” in the following areas.

Job Profile (Avg Annual Package (In Rupees))

Most employers look for data science professionals with advanced degrees, such as a Master of Science in Data Science. Candidates for data science roles usually begin with a foundation in computer science or math and build on this with a master’s degree in data science, data analytics, or a related field.
In these graduate-level programs, professionals gain core competencies in skills such as predictive analytics, statistical modeling, big data, data mining applications, enterprise analytics, data-driven decision making, data visualization, and data storytelling.

Important Certification on Data Science

1. Certified Analytics Professional (CAP)
2. Cloudera Certified Associate (CCA) Data Analyst
3. Cloudera Certified Professional (CCP) Data Engineer
4. Data Science Council of America (DASCA) Senior Data Scientist (SDS)
5. Data Science Council of America (DASCA) Principle Data Scientist (PDS)
6. Dell EMC Data Science Track (EMCDS)
7. Google Professional Data Engineer Certification
8. IBM Data Science Professional Certificate
9. Microsoft Certified: Azure AI Fundamentals
10. Microsoft Certified: Azure Data Scientist Associate
11. Open Certified Data Scientist (Open CDS)
12. SAS Certified AI & Machine Learning Professional
13. SAS Certified Big Data Professional
14. SAS Certified Data Scientist
15. Tensorflow Developer Certificate

Job opportunities in Data Science

Let us take a sneak peek into some of the Data Science job roles in demand. Data Science jobs for freshers may include the job of a business analyst, data scientist, statistician or data architect.

1. Big Data Engineer: Big data engineers develop, maintain, test, and evaluate big data solutions within organizations.
2. Machine Learning Engineer: Machine learning engineers have to design and implement machine learning applications/algorithms to address business challenges.
3. Data Engineer/Data Architect: Data engineers/architects develop, construct, test, and maintain highly scalable data management systems.
4. Data Scientist: Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing.
5. Statistician: Statistician interprets the results, along with strategic recommendations or incisive predictions, using data visualization tools or reports.
6. Data Analysts: Data analysts are involved in data manipulations and data visualization.
7. Business Analysts: Business analysts use predictive, prescriptive, and descriptive analyses to transform complex data into easily understood actionable insights for the users.