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Data Analytics, Machine Learning, Deep Learning, and Artificial Intelligence are the current buzzwords in the corporate world. The concepts were there long before, but the recent hype is due to the massive amounts of data that is getting generated daily and the enormous computational power that modern day computers hold.

However, a lot of the times all these keywords are used in the same context which is wrong and often leads to false assumptions among the individuals. Even the companies use all the terms in the same job description to lure people without even understanding the implementation of each in the industry.

The recent growth in the analytical space has drawn interest from not only engineers or statisticians but people from different backgrounds like healthcare, physics, as well. Analytics requires a blend of data intuition, machine learning, visualization, and communication which makes it possible for anyone with the right mindset to master such skills.

Data Analytics, Machine Learning, Deep Learning, AI – Where to use and Why

In this blog post, we would delve into each of these buzzwords and try to cover their meaning and usage in the present world.

Data Analytics

Data Analytics - where to use and why

Data Analytics deals with the process of descriptive statistics i.e., drawing conclusions from the data to communicate to the business which would help in making better decisions. The analysis of data could range from data quality checks to data visualization.

In layman terms, data analysts would require to get the data, clean, and transform the data and then make relevant analysis to increase the productivity of the business. To become a Data Analyst, one needs to have domain understanding along with the exploratory data analysis skills to derive accurate findings for the business.Learn Data Analytics using Python | EduGrad

A Data Analyst needs to collect data from structured sources like MongoDB, MySQL to a plethora of unstructured sources like image, social media, etc. The data needs to be relevant and consistent with the business. Once the relevant data is collected, a data analyst needs to master the tools to analyze it, as it could vary according to the organization. After the data is analyzed with the help of various tools, reports get generated based on the desired findings which are then to be presented to the stakeholders.

The skills required by a Data Analyst:

  • Statistics – To draw conclusions from the data, it’s necessary to understand the relevant statistical metric to be used on the data. Few of the statistical concepts a data analyst should master are Hypothesis testing, Bayes Theorem, p-value, and so on.
  • Data wrangling – In most cases, the data is not clean and hence it is required to clean the data to get rid of the missing values, outliers, etc., and make it ready for analysis.
  • Exploratory Data Analysis – Once the data is cleaned, it needs to be analyzed to extract findings for the business. EDA is a process to do just that.
  • Data Visualization – The most essential job of a Data Analyst is to present the data for the business to comprehend and thus visualizing it using the appropriate graphs and charts becomes a necessity.

Machine Learning

Machine Learning is the branch of Data Science where computers learn patterns from past data to make accurate predictions. Machine Learning has been present since the advent of the computer and the first application which was built using the ML algorithms was the Email Spam Filter Classification where a set of emails labeled as ‘Spam’ and ‘Not Spam’ was used to train the system which categorized a set of unknown emails as ‘Spam’ or ‘Not Spam’ later on.Learn Regression Analysis in 2 min | EduGrad

In Machine Learning, the predictions are done by a certain set of algorithms like Linear, and Logistic Regression, Decision Tree, Random Forest, and others. The choice of algorithms is based on the problem statement on hand.

Types of problems in Machine Learning:  

  • Supervised Learning – Here, each input has a corresponding output labeled in the dataset.
  • Unsupervised Learning – In unsupervised learning, the dataset is not labeled and thus the input data needs to be clustered based on certain patterns.
  • Reinforcement Learning – The model is rewarded for every correct move and penalized for any incorrect move.

Machine learning - Where to use and Why

Machine Learning is used in various industries like BFSI, Healthcare, Manufacturing, etc. Some of the use cases are – Credit Card Fraud Detection, Customer Churn Prediction, etc.

Want to explore Machine Learning Techniques?

Deep Learning

Deep Learning is the sub-field of Machine Learning which works on the principle of Neural Networks. Now, the structure of the neural nets is akin to our brain where the data is based through several layers of nets to make accurate predictions.

Unlike Machine Learning, Deep Learning requires large volumes of data and high computational power to run the algorithms.  It is used in companies like Google, Facebook which generates huge amounts of data and has the system capacity to run the algorithms.

Some of the popular frameworks in Deep Learning are TensorFlow, Keras, PyTorch, and Theano. TensorFlow is by far the most popular one. It was created by Google.

Here is an image of how actually deep learning works.

How deep learning works

Deep Learning is used in detecting, cancer cells, facial recognition, object detection, and many more.

Artificial Intelligence

The study of AI is about building smart, intelligent systems which could make human-like decisions. It focuses on success instead of accuracy and higher the complexity of the problem, the better it is for AI to solve them.

AI has eased our life to a large extent in the last decade. Some of the breakthroughs in AI are – Apple’s Siri, Amazon’s Alexa. Though AI has been around for ages, the only thing that has evolved is how we perceive AI in the modern world and use it for the betterment of society.

Below is an image of possible applications for Artificial Intelligence.

Possible Applications for Machine learning

Artificial Intelligence could be subdivided into four types:

  • Reactive AI – It reacts to an action taken at the moment, and lacks historical data to perform actions.
  • Limited Memory – In this case, the past data is repeatedly added to the memory.
  • Theory of Mind – It deals with human emotions, and psychology.
  • Self-aware – This is yet to be implemented. It is highly advanced in dealing with super-intelligent machines.

Conclusion

Data Science is a concept which involves a Data Analyst to an AI researcher. If you have the right mindset and desire to learn, this is the perfect time to master all the skills from EduGrad

Our Popular Courses on Data science –

Learn Python for Data science | EduGrad  Learn Intro to Database tools for Data Science | EduGrad

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Learn Natural Language Processing tutorial | EduGradData Visualization tools and start creating your own Dashboards | EduGrad

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