Data Science is a buzzword for a field related to business data analytics. A skill that has gained popularity following technologies such as the Internet and mobile devices. The demands for jobs in data science have increased and will be so for years to come. The growth of this field of knowledge challenges companies and employees to be aware of the significantly high amount of data transferred across devices every second. Companies can track these data to gain advantage over their competitors or make their business more profitable. The future of data science will play a vital role in the future world’s economy, if not already. Data science includes Market Analysis and Data Mining.
Data processing is a huge influence in today’s economy
Market Analysis
Starting a new business or launching a new product line can be a daunting task. Starting with a comprehensive market analysis though can help to determine the target market and the need for the concept. Once this is understood, it will be easier to develop a marketing plan without wasting valuable funds trying to get into an already over saturated market.
Business owners should consider how they plan to grow the demand within the industry. A thorough market analysis will be a blueprint for how to run the business. It is important to review the plan periodically and make changes when necessary.
Data Mining
By combining mathematical algorithms and equations with statistical techniques; specialists can extract trends and patterns that they can apply to advertising and marketing effectiveness; e-commerce initiatives, supply chain processes and many other elements of business. By extracting actionable information, companies can push forward by fine tuning processes, increasing productivity and efficiency. Data mining will help sharpen marketing drives, slicken supply chain processes and increase efficiency across the board.
We are in the information economy and its only getting bigger as data continues to grow. Consider social media: linked in, Facebook, twitter- this is fundamentally all more data that describes people: what they do, what they like, whom they are- the way to extract this abundance of information is with data mining. By not using all the information at its disposal, a company will always fall short of its potential especially when it comes to meeting the need of customers.
1. Pre-processing
Before we can use data mining algorithms , we must assemble a data set. As data mining can only uncover patterns actually present in the data, the target data-set must be large enough to contain these patterns while remaining concise enough for mining within an acceptable time limit. We can glean our own information from various Data Marts and Warehouses, or you can provide your own data sets if you have a very specific goal you are looking to achieve. Then it is time to clean the data set; data cleaning removes outlying irregularities, observations containing noise and those with missing data
2. Results validation
Specialists then validate our results Once they have extracted information via the data set. Not all patterns found by the data mining algorithms are necessarily valid. Due to overfitting from complex algorithms, we test samples again to make sure the conclusions reached are accurate.
3. Data mining
Data mining involves five common classes of tasks
• Deviation detection – The identification of anomalies in data records that might be interesting or data errors and require further investigation.
• Clustering – is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.
• Dependency Modeling – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using dependency modeling, the supermarket can determine which products people frequently buy together and use this information for marketing purposes. In other words, this is market basket analysis.
• Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as “legitimate” or as “spam”.
• Regression – Attempts to find a function which models the data with the least error.