Introduction to Data science, Applications & Advantages

Data Science:

The lifecycle shown below can help define data science. Mathematical and statistical understanding, domain knowledge competence, and fundamental computer abilities are important prerequisites.

By putting into practise the aforementioned phases, including Business Understanding, Data Acquisition and Understanding, Exploratory Data Analysis/Cleaning the Data, Model Building, Model Evaluation, and Deployment, Data Science is utilised to solve difficulties with Big Data. In order to implement the necessary business decisions from sentiment analysis or predictive analysis from the data that was collected, a Data Scientist collects data from primary data sources (survey, experiment, etc.) or secondary data sources (like RDBMS, CRM, FRM, SCM, HCM, SQL Databases, etc.).

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Applications of Data Science:

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• One of the most important applications of Data Science is in the field of E-commerce to identify consumers, recommending products, analyzing reviews.

• In Manufacturing to predict potential problems, monitoring systems, Automating manufacturing units, Maintenance scheduling, Anomaly detection.

• In Banking for fraudulent case detection.

• In Finance for customer segmentation, strategic decision making Algorithm trading, risk analytics.

• In Healthcare for medical image analysis, drug discovery, Bioinformatics, Virtual Assistant.

• In Transport for self-driving cars, enhanced driving experience, car monitoring system, enhancing the safety of passengers.

Skills Essential to Be a Data Scientist

Analytics, programming, and domain expertise are required skills which should be acquired for one to establish a successful career in this field:

Knowledge of Machine Learning algorithms

• Knowledge of Python, R

• Experience in SQL

• Ability to work with NoSQL, Hadoop, spark

• Have a grip on multiple analytical functions

Advantages of Data Science:

• It is on high demand

• Less number of people available with the necessary skillset for abundant job opportunities available in market

• Highly-paid and versatile field

• Data can be processed and analyzed to draw business insights.

What is meant by Machine Learning?

Machine learning is the application of algorithms to gather data, draw inferences from it, and then use it to predict future trends. Machine learning uses predictive and statistical analysis that may be used to create patterns.

These days' e-commerce websites use machine learning algorithms in their advertising and promotions to draw clients in. Example Swiggy targets customers who make frequent food orders, analyses time patterns, and releases a combination offer to a targeted client within a certain time frame with a limited validity, perhaps pressuring the customer to place an order. Another excellent example is Gmail, which remembers our email patterns and suggests possible words for us to enter.

For a specialist in Machine Learning:

The following skill-set helps to kick start your career as a Machine Learning expert:

• In-depth knowledge of programming

• Knowledge of Basics of probability and statistics

• Data modeling and evaluation skills

Data Science vs. Machine Learning:

Machine learning is a discipline of data science that focuses on supervised and unsupervised algorithms that take into account the relationships between variables' causes and effects. Unsupervised algorithms include pattern-finding methods including clustering, association rules, recommendation systems, and text mining principles. Supervised algorithms include regression techniques, classification techniques, and forecasting on time series data.

While Data Science is appropriate for pre-processing for exploratory data analysis, Machine Learning handles algorithms and statistical processes appropriate for numerous domains. It focuses mostly on decision-making and the execution of crucial business initiatives. Therefore, it is crucial for a data scientist to comprehend visual representations rather than simply using software tools.

In the present market, demand for data science and machine learning is high. You can obtain a niche job in this industry if you possess the relevant skill sets and practical expertise.

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Data Science vs. Machine Learning:

• Easily identifies trends and patterns

• Continuous improvement is possible

• No human intervention needed

• Handling multi-dimensional and multivariate data.

Applications of Machine learning:

• Automatic language Translation

• Medical Diagnosis

• Stock market trading

• Online fraud detection

• Virtual personal assistant

• Email spam and malware filtering

• Self-driving cars

• Product recommendations

• Traffic predictions

• Speech recognition

• Image recognition.