Call it by any name – Data Analytics or Data Science or even Business Analytics, the purpose remains to analyze the data which is being created through multiple sources. These sources can be anything from traditional databases to IoT sensors and even satellite signals etc. It would be rather easy to identify places where data is not being generated than to do vice-versa. It is overwhelming to see the pace of technological advancements which are happening today. These advancements are also contributing to data generation day-in-day-out. From wearable devices which track your pulse rate, exercising patterns, eating habits and sleeping schedules – the data is being created even when you’re not awake. It requires exceptional reasoning and skills to analyze such a large variety of data which is continuously being generated. In order to have a better understanding and cater to these needs, there are four important areas of study – Statistical Analysis, Data Mining, Forecasting and Data Visualization, Data Science.
Let’s observe them one by one:
Here are a few points which one must know about statistical analysis:
- First and foremost is Exploratory Data Analysis. The reason for this is simple, 60% of the project time is spent on exploring data science but still, it is one of those steps which even a veteran data scientist can forget about.
- Second in the order is Hypothesis Testing. This step basically determines the statistical significance of the input variable which impacts the output variable.
- Regression Techniques like Linear, Poisson, and Negative Binomial Regression. These are helpful in building predictive models.
- Implicate to deal with missing data e.g. Null values, Missing Values and NA Values etc.
Here are a few things which you must know for data mining unsupervised learning:
- Techniques used in clustering or segmentation like K-means & hierarchical clustering. These techniques help to strategies for specific groups of related things.
- Another important aspect is to learn Dimension Reduction Techniques like PCA & SVD. This facilitates effective and smooth managing of big data.
- In order to establish a relationship between various items, one must learn Association Rules or Market Basket Analysis.
- Understanding the Recommendation System which helps in recommending the next item which a customer might purchase.
- Network Analysis to understand and identify the most important person or item in the entire network.
These were the points about Data Mining Unsupervised Learning. Now, let us talk about points to remember about Data Mining Supervised Learning.
- Techniques used in predictive modelling and building classification model such as Decision Tree, Random Forest, Naive Bayes, K-NN, Neural Networks and SVM etc.
- Understanding the concepts of Artificial Intelligence and Machine Learning as they are the core of supervised learning and with IoT coming to the fore, the world will see a great demand for professionals with Data Mining Supervised Learning skills.
Forecasting/ Time Series
- To forecast sales or profits or even things like weather which are based on data ordered in time series, one must understand AR, MA, ARMA and ARIMA.
- Techniques like ARCH & GARCH are used in situations where there is high-frequency data which means data that gets generated at the high pace like stock market data.
You must know the following for Data Visualization:
- There are certain tools like Tableau which help in visualizing the data to derive meaningful inferences for the benefit of business.
- If you wish to successfully build visualizations/reports and showcase them effectively in front of stakeholders, then it becomes imperative to learn Data Visualization principles.
To become a successful Data Scientist, one should have a thorough understanding of these concepts. Cognixia runs various training programs on Big Data Analytics, R, Cloud Computing. If you wish to make a career in the field of data science or data analytics, then opt for our training programs today. For further information, you can write to us
Tag : big data, data, data science, science