What is data analysis?
Data analysis refers to obtaining, inspecting, cleaning and transforming data to gain valuable insights, and aid in taking future decisions so as to enhance efficiency of a service, identify trends in product performance and customer behavior, and provide personalized experiences to customers.
Steps in data analysis
Having an objective in mind:
Analyzing data with an objective in mind is always useful, as there will always be a certain path and the end goal will be clear in mind. Also identifying the problem before-hand is always helpful.
Extraction of data:
Once the objective is set, relevant data must be extracted to put the "data" in "data-analysis". Open source data can be obtained, or looking into research articles can also help, as technical research is accompanied with data analysis. The features in those datasets must be in accordance with the objective for the results to be useful.
Cleaning the data:
Data acquired through extraction is termed as "raw-data" and it usually is "dirty" in the sense that it has repeating values, null values, or irrelevant values. These values are collectively termed as "outliers". These outliers must be removed to from the dataset before using it for analysis for a decent accuracy on the algorithm used for analysis.
Exploratory Data Analysis (EDA):
EDA is a crucial step before the actual analysis starts, as knowing or familiarizing yourself with the data is important. Little things, like knowing the mean of a numerical feature, or understanding the distribution of the features will be a huge advantage when evaluating post-analysis outputs, as you know what to expect, and anything out of the blue can be ruled out, or used for further development of the algorithm.
EDA is where plotting graphs of features takes place. Graphs can help identify the range of numerical data and the distribution. For example, the graph below shows a normal distribution of frequency of SAT scores of students.
Choosing the right model:
When using an algorithm to analyze data, choosing the right model to suit the dataset and the desired output, is of utmost importance. For example using Long Short Term Memory (LSTM) will help in analyzing datasets where the data points depend on previous data points. Thus, it is suitable for analyzing time-series data. Knowing what model to choose is very important. If a wrong model is chosen, its accuracy will never converge and reach a suitable level.
Evaluating the model:
Model evaluation is important, as even though the accuracy achieved is good, there might be irregularities in between epochs, which can be sought out using graphs, to plot the accuracy after each epoch. Examples of accuracy metrics are Receiver Operating Characteristics (ROC) curves, and AUC (Area Under the Curve), as shown in the figure below.
What drives companies?
What keeps a company running, are its customers and the ability of a company to retain its customers. For achieving a retention rate, understanding the likes and dislikes, and the behavior of its customers is of utmost importance. This very crucial function is achieved through "data analysis".
Analyzing data is the solution to almost everything nowadays, given that today's business world revolves around data.
Decision-making, is a very important skill that companies must have, to make right decisions at the right time, which could make or break the company. Data analysis has allowed for quicker and accurate decisions. It comes in as a compass to a company, guiding it like a lost traveler finding their way.
Data is being generated in large amounts on a daily basis. So, data analysis is going to be the only way for a company to receive and analyze feedback and work towards improving, based on the results. So, data is the most valuable resource in the market, given the insights and identifying patterns in an otherwise unpredictable market. The bar graph below shows the market value of data over the years.
What is Artificial Intelligence(AI)?
I don't believe it needs as elaborate an introduction. AI is all around us today. AI can be trained to do anything with the right data, algorithms, computational resources and intention.
Data is everything to AI. AI works and trains on data. Giving it the right data and acting in accordance with its output, can prove to be tremendously lucrative. Which is why, with the recent AI disruption, quite a lot of services are making use of AI to adapt to the present market conditions, which is data-driven.
AI and data
Data, being the only thing that turns the cogs in an AI, data and AI are the perfect match for each other. Gone are the days we humans needed to sit down for hours together and analyze data like mad-men. AI has reduced all the analysis to just a one-time investment, which is the development of an algorithm. Once the algorithm is set, it is a successful black box that accepts data as input and provides valuable insights in a significantly lesser time duration, which would have taken us humans, hours for each set of data.
AI can reduce the number of steps of the data analysis process by performing steps like data gathering, cleaning and preprocessing by itself. In this context, it has been a boon to data analysis.
AI is versatile, in that it can help in every aspect of data analysis and support a data analyst with EDA and cleaning of data. Once clean data is obtained it can be employed once again to choose a suitable model, given the dataset. It can also evaluate the model. Data analysis has never been easier!
Taking a moment to reiterate, this world revolves around data. Which means, the AI disruption has brought about a whole new level of excellence a company can achieve. Now, since companies nowadays rely on data to take decisions and upgrade, AI is the perfect tool to complement them. Which is why so many companies are turning to AI.
AI along with data has endless capabilities. Data visualization is also something that is an integral part of the data analysis process. It provides us with an intrinsic understanding of the data that is going to drive the AI. This can help us predict and guess what the output of the algorithm should look like. This is a very important step, as using wrong data or being oblivious to erroneous outputs can prove fatal to a company. Since python is predominantly used to write AI algorithms, making instant graphs and understanding the data has become very easy, and a natural part of the process of data analysis.
Conclusion
To conclude, data analysis remains constant, even though new technologies may emerge. Data analysis is a very important skill which can mold the future. Using technologies such as AI is well and good, but we should never forget that it all boils down to human intuition in the end.
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