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Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

The Data Science Life Cycle

The term data scientist was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data.1 In a 2009 McKinsey&Company article, Hal Varian, Googles chief economist and UC Berkeley professor of information sciences, business, and economics, predicted the importance of adapting to technologys influence and reconfiguration of different industries.2

The ability to take data to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it thats going to be a hugely important skill in the next decades.

Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics3

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.

Advance Your Career with an Online Short Course

Take theData Science Essentialsonline short course and earn a certificatefrom the UC Berkeley School of Information.

What Does a Data Scientist Do?

In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.

Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

They must also be able to utilize key technical tools and skills, including:

R

Python

Apache Hadoop

MapReduce

Apache Spark

NoSQL databases

Cloud computing

D3

Apache Pig

Tableau

iPython notebooks

GitHub

Why Become a Data Scientist?

Glassdoor ranked data scientist among the top three jobs in America since 2016.4 As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions.5

The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2021, along with multiple data-science-related skills as the most in-demand by companies.6

The statistics listed below represent the significant and growing demand for data scientists.

Number of Job Openings

Average Base Salary

Best Job in America 2021

Sources:GlassdoorandForbes

Where Do You Fit in Data Science?

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.

Data Scientist

Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.

Skills needed:Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

Data Analyst

Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills needed:Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

Data Engineer

Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills needed:Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Data Science Career Outlook and Salary Opportunities

Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With almost 6,000 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.8

Below are the average base salaries for the following positions:9

Data analyst:$69,517

Data scientist:$117,212

Senior data scientist:$142,258

Data engineer:$112,493

Gaining specialized skills within the data science field can distinguish data scientists even further. For example, machine learning experts utilize high-level programming skills to create algorithms that continuously gather data and automatically adjust their function to be more effective.

1hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century. Accessed April 2018.arrow_upwardReturn to footnote reference2 http://www.mckinsey.com/industries/high-tech/our-insights/hal-varian-on-how-the-web-challenges-managers. Accessed July 2018.arrow_upwardReturn to footnote reference3www.mckinsey.com/industries/high-tech/our-insights/hal-varian-on-how-the-web-challenges-managers. Accessed July 2018.arrow_upwardReturn to footnote reference4 https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm. Accessed October 2021.arrow_upwardReturn to footnote reference5 https://www.discoverdatascience.org/articles/top-reasons-to-become-a-data-scientist/arrow_upwardReturn to footnote reference6 https://business.linkedin.com/talent-solutions/resources/talent-acquisition/jobs-on-the-rise-us. Accessed October 2021.arrow_upwardReturn to footnote reference7 https://towardsdatascience.com/is-data-science-still-a-rising-career-in-2021-722281f7074c. Accessed October 2021.arrow_upwardReturn to footnote reference 8 https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm. Accessed April 2018.arrow_upwardReturn to footnote reference9 http://www.glassdoor.com/Salaries/index.htm. Accessed April 2018.arrow_upwardReturn to footnote reference

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What is Data Science? | The Data Science Career Path - UCB-UMT

Data Science has become the most demanding job of the 21st century. Every organization is looking for candidates with knowledge of data science. In this tutorial, we are giving an introduction to data science, with data science Job roles, tools for data science, components of data science, application, etc.

So let's start,

Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.

It is a multidisciplinary field that uses tools and techniques to manipulate the data so that you can find something new and meaningful.

Data science uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data related problems. It is the future of artificial intelligence.

In short, we can say that data science is all about:

Let suppose we want to travel from station A to station B by car. Now, we need to take some decisions such as which route will be the best route to reach faster at the location, in which route there will be no traffic jam, and which will be cost-effective. All these decision factors will act as input data, and we will get an appropriate answer from these decisions, so this analysis of data is called the data analysis, which is a part of data science.

Some years ago, data was less and mostly available in a structured form, which could be easily stored in excel sheets, and processed using BI tools.

But in today's world, data is becoming so vast, i.e., approximately 2.5 quintals bytes of data is generating on every day, which led to data explosion. It is estimated as per researches, that by 2020, 1.7 MB of data will be created at every single second, by a single person on earth. Every Company requires data to work, grow, and improve their businesses.

Now, handling of such huge amount of data is a challenging task for every organization. So to handle, process, and analysis of this, we required some complex, powerful, and efficient algorithms and technology, and that technology came into existence as data Science. Following are some main reasons for using data science technology:

As per various surveys, data scientist job is becoming the most demanding Job of the 21st century due to increasing demands for data science. Some people also called it "the hottest job title of the 21st century". Data scientists are the experts who can use various statistical tools and machine learning algorithms to understand and analyze the data.

The average salary range for data scientist will be approximately $95,000 to $ 165,000 per annum, and as per different researches, about 11.5 millions of job will be created by the year 2026.

If you learn data science, then you get the opportunity to find the various exciting job roles in this domain. The main job roles are given below:

Below is the explanation of some critical job titles of data science.

1. Data Analyst:

Data analyst is an individual, who performs mining of huge amount of data, models the data, looks for patterns, relationship, trends, and so on. At the end of the day, he comes up with visualization and reporting for analyzing the data for decision making and problem-solving process.

Skill required: For becoming a data analyst, you must get a good background in mathematics, business intelligence, data mining, and basic knowledge of statistics. You should also be familiar with some computer languages and tools such as MATLAB, Python, SQL, Hive, Pig, Excel, SAS, R, JS, Spark, etc.

2. Machine Learning Expert:

The machine learning expert is the one who works with various machine learning algorithms used in data science such as regression, clustering, classification, decision tree, random forest, etc.

Skill Required: Computer programming languages such as Python, C++, R, Java, and Hadoop. You should also have an understanding of various algorithms, problem-solving analytical skill, probability, and statistics.

3. Data Engineer:

A data engineer works with massive amount of data and responsible for building and maintaining the data architecture of a data science project. Data engineer also works for the creation of data set processes used in modeling, mining, acquisition, and verification.

Skill required: Data engineer must have depth knowledge of SQL, MongoDB, Cassandra, HBase, Apache Spark, Hive, MapReduce, with language knowledge of Python, C/C++, Java, Perl, etc.

4. Data Scientist:

A data scientist is a professional who works with an enormous amount of data to come up with compelling business insights through the deployment of various tools, techniques, methodologies, algorithms, etc.

Skill required: To become a data scientist, one should have technical language skills such as R, SAS, SQL, Python, Hive, Pig, Apache spark, MATLAB. Data scientists must have an understanding of Statistics, Mathematics, visualization, and communication skills.

BI stands for business intelligence, which is also used for data analysis of business information: Below are some differences between BI and Data sciences:

The main components of Data Science are given below:

1. Statistics: Statistics is one of the most important components of data science. Statistics is a way to collect and analyze the numerical data in a large amount and finding meaningful insights from it.

2. Domain Expertise: In data science, domain expertise binds data science together. Domain expertise means specialized knowledge or skills of a particular area. In data science, there are various areas for which we need domain experts.

3. Data engineering: Data engineering is a part of data science, which involves acquiring, storing, retrieving, and transforming the data. Data engineering also includes metadata (data about data) to the data.

4. Visualization: Data visualization is meant by representing data in a visual context so that people can easily understand the significance of data. Data visualization makes it easy to access the huge amount of data in visuals.

5. Advanced computing: Heavy lifting of data science is advanced computing. Advanced computing involves designing, writing, debugging, and maintaining the source code of computer programs.

6. Mathematics: Mathematics is the critical part of data science. Mathematics involves the study of quantity, structure, space, and changes. For a data scientist, knowledge of good mathematics is essential.

7. Machine learning: Machine learning is backbone of data science. Machine learning is all about to provide training to a machine so that it can act as a human brain. In data science, we use various machine learning algorithms to solve the problems.

Following are some tools required for data science:

To become a data scientist, one should also be aware of machine learning and its algorithms, as in data science, there are various machine learning algorithms which are broadly being used. Following are the name of some machine learning algorithms used in data science:

We will provide you some brief introduction for few of the important algorithms here,

1. Linear Regression Algorithm: Linear regression is the most popular machine learning algorithm based on supervised learning. This algorithm work on regression, which is a method of modeling target values based on independent variables. It represents the form of the linear equation, which has a relationship between the set of inputs and predictive output. This algorithm is mostly used in forecasting and predictions. Since it shows the linear relationship between input and output variable, hence it is called linear regression.

The below equation can describe the relationship between x and y variables:

Y= mx+c

Where, y= Dependent variableX= independent variableM= slopeC= intercept.

2. Decision Tree: Decision Tree algorithm is another machine learning algorithm, which belongs to the supervised learning algorithm. This is one of the most popular machine learning algorithms. It can be used for both classification and regression problems.

In the decision tree algorithm, we can solve the problem, by using tree representation in which, each node represents a feature, each branch represents a decision, and each leaf represents the outcome.

Following is the example for a Job offer problem:

In the decision tree, we start from the root of the tree and compare the values of the root attribute with record attribute. On the basis of this comparison, we follow the branch as per the value and then move to the next node. We continue comparing these values until we reach the leaf node with predicated class value.

3. K-Means Clustering: K-means clustering is one of the most popular algorithms of machine learning, which belongs to the unsupervised learning algorithm. It solves the clustering problem.

If we are given a data set of items, with certain features and values, and we need to categorize those set of items into groups, so such type of problems can be solved using k-means clustering algorithm.

K-means clustering algorithm aims at minimizing an objective function, which known as squared error function, and it is given as:

Where, J(V) => Objective function'||xi - vj||' => Euclidean distance between xi and vj.ci' => Number of data points in ith cluster.C => Number of clusters.

Now, let's understand what are the most common types of problems occurred in data science and what is the approach to solving the problems. So in data science, problems are solved using algorithms, and below is the diagram representation for applicable algorithms for possible questions:

Is this A or B? :

We can refer to this type of problem which has only two fixed solutions such as Yes or No, 1 or 0, may or may not. And this type of problems can be solved using classification algorithms.

Is this different? :

We can refer to this type of question which belongs to various patterns, and we need to find odd from them. Such type of problems can be solved using Anomaly Detection Algorithms.

How much or how many?

The other type of problem occurs which ask for numerical values or figures such as what is the time today, what will be the temperature today, can be solved using regression algorithms.

How is this organized?

Now if you have a problem which needs to deal with the organization of data, then it can be solved using clustering algorithms.

Clustering algorithm organizes and groups the data based on features, colors, or other common characteristics.

The life-cycle of data science is explained as below diagram.

The main phases of data science life cycle are given below:

1. Discovery: The first phase is discovery, which involves asking the right questions. When you start any data science project, you need to determine what are the basic requirements, priorities, and project budget. In this phase, we need to determine all the requirements of the project such as the number of people, technology, time, data, an end goal, and then we can frame the business problem on first hypothesis level.

2. Data preparation: Data preparation is also known as Data Munging. In this phase, we need to perform the following tasks:

After performing all the above tasks, we can easily use this data for our further processes.

3. Model Planning: In this phase, we need to determine the various methods and techniques to establish the relation between input variables. We will apply Exploratory data analytics(EDA) by using various statistical formula and visualization tools to understand the relations between variable and to see what data can inform us. Common tools used for model planning are:

4. Model-building: In this phase, the process of model building starts. We will create datasets for training and testing purpose. We will apply different techniques such as association, classification, and clustering, to build the model.

Following are some common Model building tools:

5. Operationalize: In this phase, we will deliver the final reports of the project, along with briefings, code, and technical documents. This phase provides you a clear overview of complete project performance and other components on a small scale before the full deployment.

6. Communicate results: In this phase, we will check if we reach the goal, which we have set on the initial phase. We will communicate the findings and final result with the business team.

Continued here:

What is Data Science: Tutorial, Components, Tools, Life Cycle ... - Java

Newly registered Macau Data Science Application Association (MODL), also known as Macau Data Lab, to organise more meetups and initiatives focused on data science and raising the profile of data science within the Greater Bay Area.

In an announcement, the association founder and chairman, Xavier Mathieu, highlighted the new association motto to bring together data science enthusiasts or anyone curious in deepening its knowledge in the field.

Hopefully, Macau Data Lab will fulfil its mission of raising the profile of data science in GBA, and become a central meeting point in Macau for anybody interested or involved in data science, from curious individuals wanting to know more to the professionals willing to meet its peers, the seasoned researcher, the student creating his portfolio, the executive looking for growth strategies, anybody is welcome, the association announced.

Mathieu initially imported the concept in 2019 in Macau from a group he previously founded in Hong-Kong, with the first event involving the screening of a movie about data science.

The local group then proceeded to organize more events about 20 in the last 2 years gathering data enthusiasts who wish to discuss data science techniques, stories, present their work, or simply meet their peers in an informal setup.

Data science is a vaguely defined discipline, and data is literally everywhere. In consequence, there is a humongous amount of references, books, videos, articles on the net to learn theory or techniques, which can feel overwhelming for people learning, and also lots of daydreaming articles that make non-specialists believe that AI is a magic wand, leading later to disappointments, Mathieu added.

The best way to understand is to practice: there is no better way to really learn something than doing an actual project and exchanging with others. Plus this is so much funnier

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New Macau Data Science Application Association vows to raise profile of the field in the region - Macau Business

This partnership will bring UBIX Advanced Intelligence to Sapphire's 1250 Customers

ORANGE COUNTY, Calif., Sept. 8, 2022 /PRNewswire/ -- UBIX Labs, the Advanced Analytics for Business company, today announced a strategic partnership with Sapphire, the leading provider of frictionless Digital Operations Transformation software and services to over 1250 mid-market to lower-enterprise companies, to simplify and accelerate the use of Advanced Analytics and Data Science to boost and accelerate the outcomes delivered by digital operations transformation.

This partnership will bring UBIX Advanced Intelligence to Sapphire's 1250 Customers

Sapphire is the leading provider of multi-platform digital operations transformation in the UK and US and enables business to leverage the power of cloud digital platforms to transform all key business operating functions, from finance to supply chain, asset management to digital operations, and is a leading partner of SAP, ServiceNow, HxGN EAM, Infor SunSystems, and Automation Anywhere.

"Digital operations transformation is the new engine room of business differentiation and the unlimited supply of energy that enables greater agility, intelligence, speed, productivity, and efficiency and competitive advantage. UBIX provides that power. In a world where being 1% more intelligent, 1% more efficient, or 1% more predictive in running your business can deliver an exponential impact, UBIX drives that advantage" commented Chris Gabriel, Chief Strategy Officer at Sapphire.

"UBIX Labs and their rapid to deploy advanced data platform will turbo boost our customers' ability to make intelligent data driven decisions, without the huge dependencies, specialist AI and machine learning skills or complex technologies can sometimes demand. We now have a portfolio of data analytics and data science services that make us uniquely positioned to become the strategic data partner for our customers."

UBIX is an industry leading Advanced Analytics company that enables organizations of all sizes to leverage existing Customer Analytics, ERP & CRM infrastructure, blending transactional and external data to create new insights that drive intelligent action. With UBIX, business users and subject matter experts can quickly and affordably solve challenging analytics problems that are not possible without data science and AI. UBIX handles a wide variety of use cases including intelligent migration, front office, and back-office solutions.

"Through our relationship with Sapphire, more organizations can now exploit the power of UBIX for Intelligent Migration to the Cloud, preserving critical data assets and empowering an Advanced Analytics strategy," said John Burke, CEO of UBIX. "We know that leveraging UBIX's Advanced Analytics Platform, Sapphire can further strengthen its market position and emerge as a leader in delivering Intelligent Enterprise Solutions."

Together, the partnership will see Sapphire incorporate the UBIX Labs Platform into an extended offer across all its digital operating platform portfolio, and eventually lead to a Sapphire white labeled AI Services Platform powered by UBIX Labs.

UBIX is privately funded and based in Orange County, CA. For more information, visit http://www.ubixlabs.com.

Media Contact:Jack Borie760-331-9470[emailprotected]

SOURCE UBIX

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UBIX LABS AND SAPPHIRE PARTNER TO PROVIDE ADVANCED ANALYTICS TO DRIVE INTELLIGENT TRANSFORMATION - PR Newswire

To deliver strong ROI, analytics capabilities need to be able to scale. But enabling scalability across highly tailored use cases isnt always easy.

Across the modern enterprise, every team wants something slightly different from analytics. They have their own goals, own data, and own KPIsleading many teams to create and deploy their own analytics solutions with available resources.

Over time, thats created a highly fragmented analytics landscape across many organizations. Siloed solutions stagnate within individual teams and lines of business, creating an environment where:

The missing piece in environments and organizations like these is scalability. When teams push ahead with their own siloed analytics projects, the solutions they create cant scalemaking it far harder to realize high ROI from them.

Unfortunately, theres one big reason why that all-important piece is still missing across many enterprise analytics landscapes: Its tough to enable if you dont have the right strategy and support.

Three main challenges limit organizations ability to scale their analytics solutions and investments today:

#1) Disparate and inconsistent data

When an individual team builds its own analytics model, it builds around its datadesigning models that make the most of the available data sets, whatever their type or quality may be. Models created for and driven by those data sets become incredibly tough to use in different contexts, where the same type or quality of data isnt available. Theres no interoperability, so the models cant be scaled elsewhere.

#2) Low visibility and understanding across silos

If one team doesnt know about an adjacent teams existing analytics investments, they cant leverage and customize them for their own use. Siloed creation and management of analytics capabilities create cultures where people simply arent aware of where and how the organization has already invested in analyticsleading to significant duplication of effort and increased costs for the enterprise.

#3) Scalability is hard to build in retroactively

When a team identifies a new internal use case for analytics, they rarely stop to ask, how could other teams or markets benefit from what were creating? As a result, solutions are built with a single purpose in mind, making it difficult for other teams to utilize them across slightly different use cases. Instead of building a widely usable foundation, then customizing it for each team, solutions are designed for a single team at a core level, making them tough to repurpose or apply elsewhere.

See also: Moving to the Cloud for Better Data Analytics and Business Insights

Organizations need to fundamentally change how they think about, design, and manage analytics capabilities to overcome those challenges and unlock the full ROI of highly scalable analytics models and solutions.

Here are four practices helping organizations do that effectively:

Start with a standardized foundation

Each team across your organization needs bespoke, tailored capabilities to get the most from analytics. But that doesnt mean they have to build their own solutions from the ground up.

By having a centralized team create a customizable, standardized foundation for analytics, teams can create exactly what they need in a consistent way that enables interoperability and sharing of models and insights across the enterprise.

With an analytics center of excellence (CoE), for example, a centralized team can create everything each team needs for their unique use cases and add value for that team by including insights and capabilities from which adjacent teams have seen value.

Bring data science and data engineering closer together

Even today, many still view analytics as the exclusive domain of data scientists. But, if you want to enable the scalability of analytics models, data engineers need to be involved in the conversation and decision-making.

Data scientists may build the models and algorithms that generate analytical insights, but data engineers ensure a consistent, interoperable data foundation to power those models. By working closely together, they can align their decisions and design choices to help scale analytical capabilities across the business.

Zoom out and get some external perspective on whats possible

Suppose you want analytics investments to deliver broad value across your organization. In that case, your projects should start with a broad view of what analytics could help you achieve across multiple use cases. Practically, that means:

Continuously learn and improve.

Analytics always requires some degree of experimentation, and you cant realistically expect every single use case to deliver high long-term value. But, even if theyre unsuccessful, organizations should take steps to learn from each of them.

Within an enterprise, someone needs to take responsibility for learning from each use case explored. That person or team can then apply those lessons across new use cases and use them to develop assets and modules that can be reused across geographies and domains, extending, and increasing the value they deliver to the business.

See original here:

Scalability: The Key to High, Long-Term Analytics ROI - RTInsights