#dataliteracy

[data-analytics-masters]

🔍 Want to understand where data comes from in analytics?

📊 Check out these 4 Types of Data Sources every analyst should know:

✔️ First-Party – Your own data

✔️ Second-Party – Partner’s shared data

✔️ Third-Party – External purchased data

✔️ User-Generated – From your audience

💡 Learn more about data analytics and boost your skills!

✅ Why Choose Us?

✔️ 100% practical training

✔️ Real-time projects & case studies

✔️ Expert mentors with industry experience

✔️ Certification & job assistance

✔️ Easy-to-understand Telugu + English mix classes

📍 Institute Address:

3rd Floor, Dr. Atmaram Estates, Metro Pillar No. A690, Beside Siri Pearls & Jewellery, near JNTU Metro Station, Hyder Nagar, Vasantha Nagar, Hyderabad, Telangana – 500072

�� Contact: +91 9948801222    📧 Email: dataanalyticsmasters.in@gmail.com 🌐 Website: https://dataanalyticsmasters.in

[nschool]

Unlock Your Career Potential with a Data Science Certificate Program

What Can I Do with a Certificate in Data Science?

Data science is a broad field that includes activities like data analysis, statistical analysis, machine learning, and fundamental computer science. It might be a lucrative and exciting career path if you are up to speed on the latest technology and are competent with numbers and data. Depending on the type of work you want, you can take a variety of paths. Some will use your strengths more than others, so it is always a good idea to assess your options and select your course. Let’s look at what you may acquire with a graduate certificate in data science.

Data Scientist Salary

Potential compensation is one of the most critical factors for many people when considering a career. According to the Bureau of Labor Statistics (BLS), computer and information research scientists may expect a median annual pay of $111,840, albeit that amount requires a Ph.D. degree. The BLS predicts 19 percent growth in this industry over the next ten years, which is much faster than the general average.

Future data scientists can make impressive incomes if they are willing to acquire a Ph.D. degree. Data scientists that work for software publishers and R&D organizations often earn the most, with top earners making between $123,180 and $125,860 per year. On average, the lowest-paid data scientists work for schools and institutions, but their pay of $72,030 is still much higher than the national average of $37,040.

Role of statistics in research

At first appearance, a statistician’s job may appear comparable to that of a data analyst or data scientist. After all, this job necessitates regular engagement with data. On the other hand, statistical analysts are primarily concerned with mathematics, whereas data scientists and data analysts focus on extracting meaningful information from data. To excel in their field, statisticians must be experienced and confident mathematicians.

Statisticians may work in various industries since most organizations require some statistical analysis. Statisticians frequently specialize in fields such as agriculture or education. A statistician, on the other hand, can only be attained with a graduate diploma in data science due to the strong math talents necessary.

Machine Learning Engineer

Several firms’ principal product is data. Even a small group of engineers or data scientists might need help with data processing. Many workers must sift through vast data to provide a data service. Many companies are looking to artificial intelligence to assist them in managing extensive data. Machine learning, a kind of artificial intelligence, is a vital tool for handling vast amounts of data. 

Machine learning, on the other hand, is designed by machine learning engineers to analyze data automatically and change it into something useful. However, the recommendation algorithm accumulates more data points when you watch more videos. As more data is collected, the algorithm “learns,” and its suggestions become more accurate. Furthermore, because the algorithm runs itself after construction, it speeds up the data collection.

Data Analyst

A data scientist and a data analyst are similar, and the terms can be used interchangeably depending on the company. You may be requested to access data from a database, master Excel spreadsheets, or build data visualizations for your company’s personnel. Although some coding or programming knowledge is advantageous, data analysts rarely use these skills to the extent that data scientists do.

Analysts evaluate a company’s data and draw meaningful conclusions from it. Analysts generate reports based on their findings to help the organization develop and improve over time. For example, a store analyst may use purchase data to identify the most common client demographics. The company might then utilize the data to create targeted marketing campaigns to reach those segments. Writing reports that explain data in a way that people outside the data field can understand is part of the intricacy of this career.

Data scientists

Data scientists and data analysts frequently share responsibilities. The direct contrast between the two is that a data scientist has a more substantial background in computer science. A data scientist may also take on more commonly associated duties with data analysts, particularly in smaller organizations with fewer employees. To be a competent data scientist, you must be skilled in math and statistics. To analyze data more successfully, you’ll also need to be able to write code. Most data scientists examine data trends before making forecasts. They typically develop algorithms that model data well.

Data Engineer

A data engineer and a data scientist are the same people. On the other hand, data engineers frequently have solid technological backgrounds, and data scientists usually have mathematical experience. Data scientists may develop software and understand how it works, but data engineers in the data science sector must be able to build, manage, and troubleshoot complex software. 

A data engineer is essential as a company grows since it will create the basic data architecture necessary to move forward. Analytics may also discover areas that need to be addressed and those that are doing effectively. This profession requires solid software engineering skills rather than understanding how to interpret statistics correctly.

Important Data Scientist Skills

Data scientist abilities are further divided into two types.

Their mastery of sophisticated mathematical methods, statistics, and technologically oriented abilities is significantly tied to their technical expertise.

Excellent interpersonal skills, communication, and collaboration abilities are examples of non-technical attributes.

Technical Data Science Skills

While data scientists only need a lifetime of information stored in their heads to start a successful career in this field, a few basic technical skills that may be developed are required. These are detailed below Technical Data Science Skills

An Understanding of Basic Statistics

An Understanding of Basic Tools Used

A Good Understanding of Calculus and Algebra

Data Visualization Skills

Correcting Dirty Data

An Understanding of Basic Statistics

Regardless of whether an organization eventually hires a data science specialist, this person must know some of the most prevalent programming tools and the language used to use these programs. Understanding statistical programming languages such as R or Python and database querying languages such as SQL is required. Data scientists must understand maximum likelihood estimators, statistical tests, distributions, and other concepts. It is also vital that these experts understand how to identify which method will work best in a given situation. Depending on the company, data-driven tactics for interpreting and calculating statistics may be prioritized more or less.

A Good Understanding of Calculus and Algebra

It may appear unusual that a data science specialist would need to know how to perform calculus and algebra when many apps and software available today can manage all of that and more. Valid, not all businesses place the same importance on this knowledge. However, modern organizations whose products are characterized by data and incremental advances will benefit employees who possess these skills and do not rely just on software to accomplish their goals.

Data Visualization Skills & Correcting Dirty Data

This skill subset is crucial for newer firms beginning to make decisions based on this type of data and future projections. While robots solve this issue in many cases, the ability to detect and correct erroneous data may be a crucial skill that differentiates one in data science. Smaller firms significantly appreciate this skill since incorrect data can substantially impact their bottom line. These skills include locating and restoring missing data, correcting formatting problems, and changing timestamps.

Non-Technical Data Science Skills

It may be puzzling that data scientists would require non-technical skills. However, several essential skills must be had that fall under this category of Non-Technical Data Science Skills.

Excellent Communication Skills

A Keen Sense of Curiosity

Career Mapping and Goal Setting Skills

Excellent Communication Skills

Data science practitioners must be able to correctly communicate their work’s outcomes to technically sophisticated folks and those who are not. To do so, they must have exceptional interpersonal and communication abilities.

A Keen Sense of Curiosity

Data science specialists must maintain a level of interest to recognize current trends in their business and use them to make future projections based on the data they collect and analyze. This natural curiosity will drive them to pursue their education at the top of their game.

Career Mapping and Goal Setting Skills

A data scientist’s talents will transfer from one sub-specialty to another. Professionals in this business may specialize in different fields than their careers. As a result, they need to understand what additional skills they could need in the future if they choose to work in another area of data science.

Conclusion:

Data Science is about finding hidden data insights regarding patterns, behavior, interpretation, and assumptions to make informed business decisions. Data Scientists / Science professionals are the people who carry out these responsibilities. According to Harvard, data science is the world’s most in-demand and sought-after occupation. Nsccool Academy offers classroom self-paced learning certification courses and the most comprehensive Data Science certification training in Coimbatore.

[vaishaliblogsworld]

Discover why data literacy is crucial in today's world. This guide explores how understanding data analytics can enhance decision-making, critical thinking, communication, and career opportunities. Learn how to start with data analytics and embrace the power of data in your everyday life.

[maharghaideovate]

Importance of Data Literacy in the Contemporary Business DY Patil Online MBA View

Being data literate is probably the closest thing to gold in this fast-paced business world. Data has literally become the lifeblood of every industry and organization because, with data explosion everywhere in today¹s world, whoever may have optimal control over the understanding of his/her disparate pieces of information can actually make a lot of difference. This is especially important for the ones in Dy Patil Distance Learning MBA as they have real-world applications and real-life examples of what is being taught. In this second article, we make the case for data literacy and its importance in a modern MBA curriculum by examining it from five angles; these are: decision-making confident reliance on facts competitive stand gaining operational efficiency innovation good product development risk management Let's dive into each one.

1. Informed Decision-Making

At the heart of a successful business lies an effective decision-making process. Beyond bouncing pros, data literacy is what allows professionals to swim through troves of data and identify the signal by its noise. When they get themselves enrolled into one of the top distance MBA programs, they learn to understand data and draw relevant inferences that could contribute to making well-informed decisions. This is where data literacy comes in:

Understanding Trends: Analysis identifies new trends that enlighten strategy or operational changes.

Assessing Outcomes: If correct research and analytics inform decisions, many mistakes can be largely eradicated.

Leveraging the Predictive Analysis: Forecasting outcomes with a startup is a game changer, Tips for simple tools and predictive analysis can make an organization's future plans better than reacting to things.

2. Competitive Advantage

As we are in the era of data tsunami, a company's ability to leverage data plays a significant breed of competitive advantage. This also forms a main emphasis in the Dy Patil Distance Learning MBA. The professionals who get data literate will have the ability to set their organizations apart from those of their competitors. Think about these factors:

Market Analysis- Data Driven: Companies who use data can find market slots and serve them.

Insights into Customer Behavior: Through analysis of customer behavior, the organization can get personalized marketing strategies to enhance and help increase their satisfaction level.

Speed of Adaptation: Organizations that quickly interpret and act on data trends outperform those that do not.

3. Operational Efficiency

The backbone of profitability, more often than not is operational efficiency. Data literally trains people to be able to seek out waste and then drive solutions. This is especially true in MBA correspondence colleges taught on the ground of practical case studies and experience learning. Understanding the Critical Role of Data Literacy in Operational Excellence

Performance Metrics: so that KPIs are efficiently tracked, and overruns are controlled to the extent possible.

Workflow Optimization: Process improvements and overall productivity can be streamlined by analyzing how workflows are performing.

Cost Reduction: Conclusions drawn from data can help design durable, inexpensive solutions that maintain a high-quality level of service.

4. Developing Innovation and Products

It is not a buzzword; it facilitates sustained growth. This places data-literate professionals in a place to lead product development endeavors. For example, in the Dy Patil Distance Learning MBA and other programs aim for an ecosystem where it is feasible to enter data into an innovation routine. Here’s how Innovation is fueled by Data Literacy:

New Product Ideas: Empirical insights can also spark ideas for new products or add-ons to existing ones.

Continual analysis of customer feedback, leading to iterative improvements so that products are built around the needs and wishes of customers.

Changes in User Behavior — Understanding how users interact with products can reveal where opportunities for innovation and modifications lie.

5. Risk Management

In today's uncertain corporate environment, you need to manage risks effectively. Data literacy shows professionals how to cut through the inherent risks and seek out all potential threats by parsing, sorting, and interpreting their consumer data. Case studies and simulations revolving around risk are a significant part of the coursework in top distance MBA programs, which aim to develop a data-driven perspective on what strategy risks they can take. Key points include:

Identify Risks: By using advanced data analysis, it is possible to catch trends that may lead to upcoming risks.

Mitigation Strategies: Data-based insights help organizations to frame strategies to reduce risks effectively.

Regulatory Compliance: The right data makes it easy to meet compliance parameters when working in the face of evolving rules and provides organizations with a system within which they can operate legally.

Conclusion

To summarize, data literacy is not merely a nice-to-have skill; it's an imperative in modern business. This could be clearly seen through the Dy Patil Distance Learning MBA, where possessing data literacy enables our learners to make integral data-driven decisions thereby taking a step towards business success & sustainability and staying ahead of competition, enhancing operational efficiencies along with promoting innovation while managing risks.

Get better with data — do it now and see how easily you take advantage of these opportunities across your life!

[datamanagementeducation]

Metadata Training Courses

Join Data Management Education metadata training courses & classes to enhance your skills in metadata. Our course enables you to learn about metadata management in a short time and earn valuable certificates in this course. Enroll now!

[mastercoursesacademy]

What is Quantitative Awareness? Mind-Blowing Insights Revealed!

[frootflies]

This isn't a NORMAL distribution it's UNIFORM distribution

THIS is a normal distribution:

#KNOWYOURSTATS!!!!!!!!!!!!! #DATALITERACY!!!!!!!!!!!!!

[kajol1991]

The Trust Equation: How Self-Service Data Management Builds Trust and Enables Better Decisions

In today's data-driven world, valuable insights often get trapped in isolated silos, hindering informed decision-making. What if there was a way to empower everyone in your organization, regardless of technical background, to leverage the power of data?

Introducing the concept of Self-Service Data Management (SSDM) - the key to unlocking a new era of data-driven decision-making.

SSDM breaks down data barriers, placing the reins in the hands of business users.

Imagine:

Marketing teams crafting data-driven campaigns with real-time customer insights.

Sales reps identifying high-potential leads without relying on IT.

Everyone making data-driven decisions to improve efficiency and agility.

This blog explores the transformative potential of SSDM, including:

The Power of Access: Why Data Democratization is Essential

Strategies for Successful SSDM Implementation

A Glimpse into the Future: The Rise of the Citizen Data Scientist and the Era of Intelligent Automation

Empowering a data-driven culture unlocks a wealth of benefits:

Faster, more agile decision-making

A culture of data ownership and accountability

Enhanced innovation and problem-solving

Improved customer satisfaction and profitability

Don't wait! Read the blog to discover how SSDM can transform your organization.

#datawarehouse #datadrivenculture #analyzedata #datasources #datadrivendecisions #datagovernance #datavisualizations #dataliteracy #dataaccess

[techopinions]

Let's talk about: Online Data and Safety

the biggest thing going around right now is tiktok. everyone is scared of the ban but tiktok will not be banned and I'm gonna tell you why.

tiktok isn't harvesting your data more than Facebook and Instagram. If you go to your data on facebook you'd be surprised about how much data they get from you compared to tiktok. if tiktok were really stealing data from government officials why wouldn't the officials be at fault? government officials are NOT to have classified data on their personal devices, as they have separate devices for separate occasions. tiktok is just a basic app, if any apps are a violation to your privacy, it'd be the current social media apps you have with a few suggestions (Telegram, Snapchat, etc.).

Now, on to the data part, guessing someone's password can be pretty easy if you know what they are interested in, yesterday Donald Trump's Twitter acc was hacked because of the password he used. Please keep all your data safe by turning on 2fa, having a strong password (at least 8 letters, special chars. and, numbers) this will ensure that all your data is safe in the cloud!

Stay safe folks! ♡

http://instagram.com/cashsaesthetics

[jillyoe]

(Jill Yoe Graves)

Check our newest podcast session with Data4Change’s Head of Design, Michael Brenner, speaking about connecting social change organizations with designers, journalists, and technologist to create data-driven solutions addressing some of the world’s most pressing problems. In our conversation, Michael shares some of the teams’ incredible work in response to Covid-19, including their global work supporting grassroots efforts and civil society organizations, alongside team’s pivot to previous ways of working, new mindfulness and responding to learnings during this surreal pandemic. https://www.data4chan.ge/ And great tip about guidelines from @WuTang to "protect yo neck" against Corona Virus ... #growthThroughCollaboration #knowledgeSharing #covidResponse #designForChange #dataDrivenSolutions #PositiveSocialImpact #design #covid #response #data #podcast #leadership #innovation #mindfulness #designthinking #humanRights #WorldHealthOrganization #bigData #thickData #CreativeDisruption #DataLiteracy #globalgoals #datajournalism #dataDrivenAdvocacy #dataEthics #civilSocietyOrganizations #SocietyDrivenDesign #timeBank #careGiving

[techgirlomission-blog]

[vaishaliblogsworld]

Discover the importance of Data Science in modern education. Learn why students should gain Data Science skills to prepare for future careers, develop critical thinking, and understand the world better.

[decadentnightmaregalaxy]

Online training program: “Basic Data Literacy for persuasive communication & presentation"

Online training program: “Basic Data Literacy for persuasive communication & presentation”

This is a sample of the online training program prepared for business persons who want to learn basic data literacy. I will provide the program to a company in Bangkok, Thailand in the next month. If your organization/institution is interested in those programs, please contact me. THE OUTLINE OF THE PROGRAM: Objective and outline To learn basic data literacy required before data analytics.…

[choobo-koojaryong]

[20200924.화상대면토론수업] 어제 국가공무원인재개발원에서 구루미 플랫폼을 이용하여 온라인 토론수업을 파일럿으로 진행했다. 주제는 ‘데이터리터러시 역량강화’. 질문, 투표, 소그룹토의, 발표, 피드백을 슬라이도, 패들렛, 구글설문지, 그리고 구루미를 이용하여 화면 전환으로 퍼실리테이션 유도. 만족스럽지는 못했지만 새로운 시도로 의미있는 출발이라 생각한다. 공무원 세계에도 변화의 물결이 넘친다는 점에서 새로운 희망을 걸어본다. #진천 #국가공무원인재개발원 #구루미 #slido #padlet #dataliteracy https://www.instagram.com/p/CFiZbAjlrtr/?igshid=uah27gjmi2nl