Introduction
Data science and machine learning are the two most burst words in the industries these days. Today we will learn about data science versus machine learning. Data science and machine learning are used in conjunction. But if you are looking to build a career in these domains it is important to know the difference between them with data getting generated at a massive. Data Science vs Machine Learning
What is Data Science
Data science and machine learning technologies have become increasingly important in the industries to draw value. Out of these data and derive insights both data science and machine learning complement each other but understanding how they work is important.
let’s first begin by defining what is data science data science is a field of study that largely deals with the use of modern tools. And techniques to process clean analyze and visualize large data sets the data collected by companies can be in various formats. It could be structured semi-structured or unstructured in nature data science helps to get valuable information from this data.
Data scientists are professionals who convert raw data into meaningful insights find market patterns and help organizations to take important decisions data science is extensively used in companies such as amazon netflix airbnb and ibm.
What is Machine Learning
let’s understand what is machine learning machine learning is a field of artificial intelligence. That allows machines to learn from vast volumes of past data and make intelligent decisions on their own using algorithms.
It gives computers the ability to learn without being explicitly programmed machine learning helps to train models that learn automatically. And improve with experience companies such as google facebook apple and philips use machine learning to build ai systems now let’s look at the
What is Deep Learning
Relationship between these technologies. Data science is a broad domain that covers ai machine learning as well as deep learning. Aachine learning is a subset of artificial intelligence and it covers another sub technology called deep learning deep learning is a part of machine learning. That uses artificial neural networks to train models it works based on the structure and function of a human brain
Google Trends
Now here you can see the google trends for data science and machine learning in the united states over the past five years the graph depicts. That the search volumes are really high and both data science and machine learning are very much in demand these days people are searching. For these terms on google on youtube and other platforms and want to learn about them next
Data Science Steps
let us understand the different data science steps the first step in data science is to understand the business objectives define the goals . And find a lucrative solution the next step in the process is to collect the right data that is relevant to the problem at hand the data can be in various forms. And from different sources up next we have data wrangling this process helps to convert the raw data into another useful format.
That would be more appropriate for analytics the fourth step in the process is data exploration exploratory data analysis is used to extract valuable information from the data . And find unseen patterns data modeling is the process of creating intelligent models using sophisticated algorithms. The result of the model will help companies to solve complex problems and make decisions and the final step is data visualization where data scientists visualize the data and forecast future trends
Machine Learning Steps
Moving on to the machine learning steps the first step in the process is to import the data the data that is used for analysis can be a text file an excel file a dot csv file or it could be present in a github repository. As well the next step is to filter and clean the data the data that is used for creating machine learning models is mostly messy containing missing values.
And is not fit for analysis hence it is important to clean the data before use then it is important to select the right machine learning algorithm based on the problem that we want to solve. In the next machine learning step you should train your model and then test your model. With new data points the final step is to improve the efficiency of the model and optimize its accuracy let us talk about the main
Main Objective
objective of data science and machine learning
So data science is used to find unseen patterns in the data and discover hidden trends using data mining techniques data wrangling exploratory data analysis statistical analysis. As well as data visualization while machine learning majorly focuses.
On using machine learning algorithms to build predictive models and forecast future trends. It is used to classify the result of a new data point machine learning uses supervised unsupervised and reinforcement learning methods to solve problems with that
Tools
let’s move on and learn about the popular tools used for data science and machine learning some of the tools are common to both data scientists. And machine learning engineers use a combination of tools applications principles and algorithms to make sense of data sas.
And python are widely used programming language. and data science we also have the apache spark framework data visualization software. Such as tableau and databases such as mongodb and mysql.
Now talking about the tools and software used for machine learning we have python scikit learn amazon lex as well as microsoft azure ml studio we also have libraries such as numpy pandas scipy tai divorce and others
Applications
let’s look at the applications of data science the self-driving vehicle is a common example of data science. Data science is also widely used in the healthcare industry for drug discovery analyzing patient records and building. Medical instruments facial detection systems and fraud detection in the banking domains are other examples where data science is used coming to the machine learning applications.
We have house price prediction using algorithms like linear regression. You can create an email spam filtering system using machine learning. Next we have stock price prediction and vehicle routing optimization systems to name a few now
Skills
let us discuss the skills that a data scientist and a machine learning engineer should possess . So a data scientist should be good with databases and sql. They should have knowledge of mathematics and statistics data scientists need to have hands-on experience with programming languages . They need to know techniques related to data mining data wrangling data visualization as well as machine learning.
Now talking about the skills for a machine learning engineer this would be good with programming languages such as python and r they must be well versed with mathematics and statistics. They need to know various machine learning algorithms. Such as linear regression logistic regression support vector machines k nearest neighbors k-means clustering etc
Natural language processing deep learning and data modeling are other essential skills a machine learning engineer should possess now moving ahead
Salaries
To the final section of this video in data science versus machine learning . we have salaries so according to glassdoor the average salary of a data scientist in the united states is hundred and thirteen thousand dollars. While in india you can earn nearly nine lakhs seventy four thousand rupees per annum now a machine learning . Data Science vs Machine Learning
Engineer can earn around hundred and fourteen thousand dollars per annum in the united states. And in india the average salary is seven lakh seventy one thousand rupees per annum. Now that brings us to the end of this video on data science versus machine learning. Data Science vs Machine Learning
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