Here’s some different news from the usual this time of year.

According to ESPN Sports Analystics writer, Seth Walder, the Chargers are hiring Alex Stern to be a team analyst. Stern took part in the NFL’s Big Data Bowl in 2020, which is described by event’s official site as a “annual sports analytics contest that challenges members o the analystics community — from college students to professionals — to contribute to the NFL’s evolution of the use of advanced analytics.”

The event is sponsored by Amazon Web Services (AWS).

According to the page, contestants use a combination of traditional football data and Next Gen Stats to analyze current trends and varying player performances while attempting to push the limits of the way football is both played and coached. At the end of the day, the event hopes to “engage and empower the football analytics community to drive innovation.”

This isn’t the first move made by the team this offseason that has signaled a major shift in how the club views analytics and their importance in creating a winning edge. While Brandon Staley was putting together his coaching staff earlier this year, he also hired Cody Cejda, a former analytics member of the Northwestern football team, to be an assistant and analytics sounding board on game days.

The move may not make waves with its’ headline, but small behind-the-scenes moves such as these could add up when this season gets underway.

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Big Data is considered as one of the most trending and emerging technologies. With the increase in data, organisations are adopting these technologies to gain better insights from the data. Big Data analysts help organisations to fulfil their needs by curating valuable insights from raw data. In this article, we list down 6 must-have skills in order to become a skilled Big Data analyst.

1| Multi-Programming Skills

The fundamental knowledge of data structures and algorithms is very crucial to learn before you start to learn the other skills and it will help you throughout your journey. To be a good Big Data analyst you must know and understand the statistical languages such as Python and R.

You must learn how to code and be able to write, understand and correct errors in the code which includes a massive amount of data. Besides Python and R, there are other programming languages such as Scala, C++, SQL, Java, etc. which will be benefiting you in your journey.

2| Data Visualization

The easiest way to understand a concept is through visualization. Big data analysts working with a large number of both structured and unstructured data helps an organisation to view the analytics of the data by presenting it visually. At the present scenario, there are various prominent data visualization tools such as Tableau, Data Wrapper, Plotly, etc. It is basically an easy way to convey the results of the large dataset in a simple and understandable manner.

3| Quantitative & Analytical Skills

Quantitative and analytical skills play a major role in Big Data analytics. Knowledge in statistics, as well as mathematics, will guide while curating a large amount of unstructured data. One must have a strong grasp in linear algebra to be ahead while tackling big data problems. Quantitative data analysis methods such as descriptive statistics and inferential statistics help the analysts to summarise the data, generalise results, find patterns, make predictions, etc.

4| Data Handling & Interpreting

Managing and interpreting data is not an easy task. With the help of data interpretation, the analysts can review the data for the purpose of arriving at the inference. Handling the data and making sense out of it indeed consumes a lot of time but it can be said as one of the crucial parts of a project. The data in an organisation is growing exponentially day-by-day and handling those to derive insights need a skilled big data analyst.

5| Knowledge Of Multiple Technologies & Frameworks

We already mentioned that understanding multi-programming languages play as one of the vital to be a good Big Data analyst. Besides this, there is a range of technologies that a skilled Big Data analyst must know. Frameworks such as Apache Hadoop, Apache Spark, etc. helps in streaming Big Data to a greater extent. Components of Apache Hadoop such as HIVE, MapReduce, HDFS, Pig are highly demanding these days. It is crucial for Big Data analysts to be familiar with these technologies and frameworks for better decision-making.

6| Business & Problem Solving Skills

A good Big Data Analyst must have adequate knowledge of the business process along with statistical and technical knowledge. He must be able to understand the aspects and the business goals in an organisation before gaining insights and pattern from a large amount of data.

Bottom Line

Learning these skills will easily help you to create a perfect resume while applying for Big Data analyst jobs but you can’t be fruitful to an organisation unless you get your hands dirty by these skills. “Practice makes a man perfect”, thus the more you practice with these skills, the more you gain insights in this domain. A few links of careers in this domain have mentioned below.

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loud computing leaders Amazon.com (AMZN), Microsoft (MSFT) and Google, along with internet giants, have the inside track in monetizing artificial intelligence technology, a Mizuho Securities report says, but enterprise software providers also will grab a piece of the action.

At a basic level, artificial intelligence is the use of computer algorithms to attempt to replicate the human ability to learn, reason and make decisions.

Amazon, Microsoft and Google-parent Alphabet (GOOGL) are pushing into AI-as-a-service. They plan to rent AI tools to customers on a pay-as-you-go basis via their public cloud-computing services.

Enterprise software providers such as Salesforce.com (CRM) and ServiceNow (NOW) are in the early stages of embedding AI tools in existing products to make them more predictive, analysts say.

“In evaluating recent artificial intelligence/machine learning trends and innovation put forth by enterprise vendors, we see incremental benefit for Salesforce, ServiceNow and Splunk (SPLK),” Mizuho analyst Abhey Lamba said. “Across the stack, we think the revenue opportunity lies with application vendors that address specific business problems or analytical tool providers that help enable a solution. The core AI engine layer of the stack will likely be dominated by large vendors such as Amazon, Apple (AAPL), Facebook (FB), Google and Microsoft; smaller players are likely to either get acquired or run into significant headwinds.”

AI, around since the 1960s, now comes in new forms. The two most mentioned are “machine learning” and “deep learning networks.” The basic idea is to make better predictions by analyzing massive amounts of data.

Analysts expect companies spanning banks and finance, health care, energy, retail, agriculture and other sectors to increase spending on AI technology.

Salesforce.com introduced its “Einstein” AI cloud platform in September 2016. Einstein tools identify and prioritize sales leads and recommend products and pricing options, making it easier for sales reps to convert leads to sales.

“Salesforce.com continues to be one of the biggest proponents of machine learning, with early organic investments plus robust M&A,” Lamba added. “Einstein is seeing strong interest from customers but production deployments could take some time. ServiceNow is likely to sustain momentum as it pushes for deeper enterprise automation across functional areas with investments in product.”

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