How to make data work for your small business.

There’s an appetite for data science amongst businesses of all sizes. While buzz is one thing, the fact remains that very few – especially SMEs – have actually employed it to capitalise on the competitive advantage it brings.

Some that do make the leap end up disillusioned if it doesn’t deliver what they’d hoped. There can be a number of reasons for this, but the main one is that the business simply isn’t ready for data science.

Let’s first be clear on where the value in data science lies. While reporting processes are great, they are generally limited to revealing only what has already happened. The real value in data science lies in being able to shift focus to what will or should happen and crucially, being able to take action to change the outcome.

Data science can help businesses build strategies that factor in revenue growth over time, rather than chasing short-term gains. However, before making any investment, it’s worth asking some key questions and replying honestly. Otherwise, any efforts can end up being a flirtation rather than a lasting relationship.

So, consider the following questions; each one that you can answer ‘Yes’ to puts you one step closer to being data science ready:

Do you have sufficient historical data across each of your data sources? 

Much like the real world, we need to have enough context to predict whether our decisions will have a successful outcome or not.

Training a data science model is somewhat like training a puppy. ‘Good behaviour’ – in this case accuracy and performance – should be rewarded to encourage repetition.

Historical behavioural data and outcome data are the only treats a machine learning model needs, the more it has and by learning the relationship between the two, it gets better at making predictions.

However, the data used for training the algorithm must be representative over time in order that it can discern ‘business as usual’. So, data gathered over the course of the pandemic won’t offer a meaningful basis for data science. If all you have is anomalous data, the goalposts are constantly changing.

Also, the devil is in the detail so ensure your CRM system doesn’t overwrite point-in-time customer data to provide just the current snapshot of your customer database.

The same is true for stock data – you need to be aware of stock-outs if you want to distinguish between when there was ‘no stock’ versus ‘no demand’ as one example.

Is your data infrastructure solid?

Not all data is created equal. Making sense of it across all sources – both from the cloud and data that’s held in internal systems – means ingesting them all into a centralised data warehouse as a single point of truth to be used by all internal departments.

The expression ‘garbage in, garbage out’ holds true when we talk about data science models. Ensuring they can decipher data successfully requires ‘cleaning’ it by standardising formats, de-duplicating it and so-on.

Most importantly, there needs to be a common ID that makes it possible to join up data across different sources. For example, if we want to combine a customer’s transaction behaviour, browsing patterns and engagement with email marketing campaigns, we need an identifier.

Do you have access to expertise?

We often hear data is the new oil, but oil has limited use until it is refined. Extracting data’s true potential requires a willingness to invest in data scientists and technologies. Since data science is highly specialised, it cannot be palmed off on the marketing, or the IT team. It’s not fair on them and is very likely to doom any project to failure.

Any sources of inconsistency or friction points in the data will surface false positives and can lead to ‘data drift’, a scenario in which fundamental changes in the way customers behave over time can render models ‘stale’ and the performance degrades.

Consequently, both the data and the models need to be constantly monitored and occasionally retrained. This takes time, expertise and engineering behind the scenes.

Do you know what specific challenges you need to solve?

Advances in data science and machine learning are still a long way from ‘artificial general intelligence’. Their most successful use cases will have quite specific applications, where decision making processes are manual and available data is being under used in that process. In the simplest terms, you have to ask the right question(s).

Most typically, these applications centre around factors like lead generation and conversion, customer acquisition and retention, and stock control. So, data science has to be grounded in strategic thinking and ideally should be cumulative to build across departments and agreed business-wide goals. So, consider how you’ll be able to keep expanding on each set of results by asking the right follow-up questions.

It helps to adopt a ‘walk before you can run’ approach by focusing on the most pertinent problems or use cases first. If you have enough pointed use cases, these naturally come together to form a solution which delivers tangible value. Otherwise, you’re just doing data science for the sake of data science.

Do you have a data culture?

A data culture is one in which all key stakeholders within the business are comfortable enough to trust in the data and outputs from predictive algorithms. The stumbling block is typically internal politics.

Some teams jealously guard ‘their’ data. It’s perhaps ironic that data democracy requires everyone to get over the politics and recognise shared goals over individual or departmental ones.

Doing data science right requires time, investment and ongoing management, it often needs a change in perspective. A data culture must be led from the top and requires the Senior Leadership Team to recognise the benefits of full transparency from a shared data source. If you’re serious about data science. strong leadership is a prerequisite.

Data science is by no means a fad nor a nice-to-have, it will become increasingly business critical in a crowded market. If you are new to data science, the good news is that there is usually a lot of low-hanging fruit, you just need to know where to look for it.

The post Is Your Business Ready For Data Science? Five Questions To appeared first on Artificial Intelligence.

Here are the 10 machine learning interview questions that will help you land your dream job

Businesses are striving to make big data more worthy by adopting new disruptive technologies like artificial intelligence and machine learning. The influence of these modern technologies in sectors like banking, healthcare, manufacturing, telecommunication, etc. has drastically increased in the past few years. Well-known job roles including data scientist, artificial intelligence engineer, and machine learning engineer are always on-demand. Machine learning is a futuristic technology that lays out the basic structure models by constructing algorithms. These algorithms help machines carry out tasks without being explicitly programmed. Fortunately, the stance of machine learning in a business environment has surged the need for machine learning engineers. However, cracking a machine learning interview is not easy. Especially, big tech companies expect candidates to be technologically sound and talented. Analytics Insight has listed top machine learning questions and answers that will help you land your dream job.

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What is machine learning?

Typically put, machine learning is a method of data analysis that automates analytical model building. By using machine learning, systems can learn from data, identify patterns, and make decisions with minimal human intervention. While artificial intelligence is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. For example, robots are programmed to perform tasks based on data they gather through sensors. Machine learning helps them automatically learn programs from data.

What is the difference between data mining and machine learning?

Both data mining and machine learning revolve around big data. Since most of their functionalities are related to large datasets, they are often confused as the same thing. However, they are totally different. Machine learning is a futuristic technology that is used to study, design, and develop algorithms, which gives computers the capability to learn without being explicitly programmed. On the other hand, data mining is used to extract useful data from unstructured data that comes in different forms including texts, documents, videos, images, etc. Data mining helps businesses extract knowledge or unknown interesting patterns, and during this process, machine learning is used.

What is the difference between supervised and unsupervised machine learning?

Both supervised and unsupervised machine learning is important to train algorithms. But the difference is that supervised learning requires sorted or labeled data. Therefore, before using supervised learning, a company should do the classification process and label data groups. But unsupervised learning doesn’t require being sophisticated like that. It can work on unlabeled data explicitly. A model can identify patterns, anomalies, and relationships in the input data.

What is overfitting and what can be done to avoid it?

Overfitting is a critical situation that takes place when a machine learning model is well-versed in a dataset. It takes up random fluctuations in the training data as concepts and fails to generalize the content. Therefore, machine learning models shield themselves from applying the concept to new data. When a model is fed with properly trained data, it shows 100% accuracy. But things change when it is trained with test data. The clarity in the machine learning model shifts, resulting in errors and low efficiency, which altogether turns out as overfitting.

In order to avoid overfitting, companies should use simple models that have lesser variables and parameters. In this case, the variance can be reduced. They should also regularize the training process.

What is dimension reduction in machine learning?

Generally, dimension reduction is the process of reducing the size of the feature matrix. Fewer input dimensions often mean correspondingly fewer parameters or a simpler structure in the machine learning model, referred to as degrees of freedom. Since a machine learning model with too many degrees of freedom is likely to overfit the training dataset, dimension reduction is used to lower the chances. Dimension reduction in machine learning represents the effort to reduce the number of columns in it. By doing so, companies get a better feature set either by combining columns or by removing extra variables.

How to handle an imbalanced dataset?

When you are taking a classification test and 90% of the data is in one class, it is called an imbalanced dataset. This leads to accuracy disruption. An accuracy of 90% can be skewed if you have no predictive power on the other category of data. Therefore, to stop such scenarios from happening, companies can collect more data to balance the imbalanced dataset. They can also resemble the dataset to correct the imbalances and try different algorithms. But most importantly, you should be aware of the damage an unbalanced dataset can cause and act accordingly.

What is the confusion matrix in machine learning?

Confusion matrix, also known as error matrix, is a designated table used to measure the accurate performance of a machine learning algorithm. Classification accuracy alone can be misleading if you have an unequal number of observations in each class or if you have more than two classes in your dataset. Calculating a confusion matrix can give you a better idea of what your classification model is getting right and what types of errors it is making. Mostly used in supervised and unsupervised learning, the confusion matrix has two specific parameters namely actual and predicted.

The post INTERVIEW READY: FREQUENTLY ASKED MACHINE LEARNING QUESTIONS & ANSWERS appeared first on Artificial Intelligence.

You may have heard that the field of data science is comparatively new. You may have also heard that there is a high demand for professionals and the trend is likely to continue for the next decade or so. All these are definitely tempting and but are not enough to make an informed decision. As with any other degree, you cannot randomly become a data scientist without knowing the full story. You must judge whether this is the right career path for you and know what all comes after you have graduated.

To ease your process of selection and decision making, this post compiles a set of essential questions that you should get answers to before starting your career in Data Science. These answers will clear the picture for you and help you to take the first step right while joining the top data science courses after 12^th.

What do you do as a data scientist?

A common misconception that goes around is that the work of a data scientist is only about coding. The statement, in fact, is only partially true as the role of a data scientist is yet to be properly defined. Companies hire data scientists under different roles but the core job remains the same: deal with and analyse a massive set of data that will provide value to the company’s business goals.

By analysing data, you will have to solve matters such as how to increase the current productivity, improve product quality and customer satisfaction, and reduce the manufacturing time and so on. To summarise, you will have to increase the business’ revenue using data.

How much coding should you learn?

While data science may not only be about coding, it is definitely an integral part. Coding comes in during the mathematical processes of dealing with large quantities of data that you simply cannot solve manually.

For instance, if a course is teaching you Python, try to learn this language to an expert level. Even if you face any other language during your job, the core principles of using them remain the same. So, you may not know five different languages but you can still land a top job.

Who hires data scientists and what is the pay like?

Data scientists are required everywhere. You can get into fields such as technology, marketing, financial services, corporate setting, government services, healthcare, gaming and so on. In fact, tech companies hire most of the data scientists (41 per cent) followed by marketing companies (almost 13 per cent). To give you a clearer idea, Google, Facebook, Amazon and Flipkart hire data scientists on a regular basis.

As the demand is high and the number of professionals available is low, the pay in this field is generally high. You can easily expect a six-figure annual income once you graduate and that can easily go up to seven within a matter of two years. Depending on your skills, you can get any amount you want.

What does the future look like?

Data science has already been named the top job in America for the year 2016. Statistics also suggest that during the decade 2014-2024, the field has an expected growth rate of 11-14 per cent. Also, almost 80 per cent of the data scientists out there suggest that there is indeed a shortage of professionals in this current field. All these establishes the fact that data science is, in fact, the next big career option.

But the field is also prone to automation as most of the task is ultimately done by machines. Algorithms can run bulk quantities of data through tools and produce faster results than any human possibly can. However, this does not mean that machines will replace data scientists totally. The courses will teach you to understand the automatic algorithms, better their technology and use your creativity to invent new techniques. If you learn to find optimal solutions for a problem at hand, you will never be searching for jobs.

To conclude

If you are fresh out of the 12^th standard now, there cannot be any proper time to start your career in data science. Both new and old companies are starting to realise the importance of this field and investing without limits on the right professionals. Even universities have branches who recruit scientists to help firms with their data or develop tools to process things better and faster.

Seize the opportunity, select the right institution and kick start your career. You will be dealing with real-world data sets and the scope for growth is limitless. Data science is indeed the next big thing.

The post Data Science: Essential Questions You Need To Answer Before Starting Your Career appeared first on Artificial Intelligence.

You may have heard that the field of data science is comparatively new. You may have also heard that there is a high demand for professionals and the trend is likely to continue for the next decade or so. All these are definitely tempting and but are not enough to make an informed decision. As with any other degree, you cannot randomly become a data scientist without knowing the full story. You must judge whether this is the right career path for you and know what all comes after you have graduated.

To ease your process of selection and decision making, this post compiles a set of essential questions that you should get answers to before starting your career in Data Science. These answers will clear the picture for you and help you to take the first step right while joining the top data science courses after 12^th.

What do you do as a data scientist?

A common misconception that goes around is that the work of a data scientist is only about coding. The statement, in fact, is only partially true as the role of a data scientist is yet to be properly defined. Companies hire data scientists under different roles but the core job remains the same: deal with and analyse a massive set of data that will provide value to the company’s business goals.

By analysing data, you will have to solve matters such as how to increase the current productivity, improve product quality and customer satisfaction, and reduce the manufacturing time and so on. To summarise, you will have to increase the business’ revenue using data.

How much coding should you learn?

While data science may not only be about coding, it is definitely an integral part. Coding comes in during the mathematical processes of dealing with large quantities of data that you simply cannot solve manually.

For instance, if a course is teaching you Python, try to learn this language to an expert level. Even if you face any other language during your job, the core principles of using them remain the same. So, you may not know five different languages but you can still land a top job.

Who hires data scientists and what is the pay like?

Data scientists are required everywhere. You can get into fields such as technology, marketing, financial services, corporate setting, government services, healthcare, gaming and so on. In fact, tech companies hire most of the data scientists (41 per cent) followed by marketing companies (almost 13 per cent). To give you a clearer idea, Google, Facebook, Amazon and Flipkart hire data scientists on a regular basis.

As the demand is high and the number of professionals available is low, the pay in this field is generally high. You can easily expect a six-figure annual income once you graduate and that can easily go up to seven within a matter of two years. Depending on your skills, you can get any amount you want.

What does the future look like?

Data science has already been named the top job in America for the year 2016. Statistics also suggest that during the decade 2014-2024, the field has an expected growth rate of 11-14 per cent. Also, almost 80 per cent of the data scientists out there suggest that there is indeed a shortage of professionals in this current field. All these establishes the fact that data science is, in fact, the next big career option.

But the field is also prone to automation as most of the task is ultimately done by machines. Algorithms can run bulk quantities of data through tools and produce faster results than any human possibly can. However, this does not mean that machines will replace data scientists totally. The courses will teach you to understand the automatic algorithms, better their technology and use your creativity to invent new techniques. If you learn to find optimal solutions for a problem at hand, you will never be searching for jobs.

To conclude

If you are fresh out of the 12^th standard now, there cannot be any proper time to start your career in data science. Both new and old companies are starting to realise the importance of this field and investing without limits on the right professionals. Even universities have branches who recruit scientists to help firms with their data or develop tools to process things better and faster.

Seize the opportunity, select the right institution and kick start your career. You will be dealing with real-world data sets and the scope for growth is limitless. Data science is indeed the next big thing.

The post Data Science: Essential Questions You Need To Answer Before Starting Your Career appeared first on Artificial Intelligence.