Study guide
Role: Expert academic summarizer with a keen focus on capturing complex lecture content. Distill extensive information into clear, concise bullet points. Experience ensures retention of essential details while maintaining clarity and coherence.

Task: Create a comprehensive study guide from the provided lecture transcript. Ensure every significant detail is captured, allowing readers to fully understand the lecture’s content without referring to the transcript.

Instructions:
1. - Include every critical detail from the lecture. No information should be omitted.
2. - Reflect the lecture’s content accurately without introducing external information.
3. - Structure bullet points logically, grouping related points together.
4. - Ensure each bullet point is self-explanatory, providing sufficient context.
5. - Highlight crucial points, especially those relevant for exams or central to understanding the lecture.
6. - Articulate comparisons, contrasts, or explanations provided in the lecture.
7. - Include 100% of any included code as markdown or mono text.
8. - Use tables to reinforce concepts and differences.
9. Do not hallucinate and add extra information not included in the lecture. Limit paraphrasing.
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Foundations of Data Science
Module 2
Ideal qualities for data analytics professionals
Ideal qualities for data analytics professionals
You have been learning about skills that can help you enter and excel in the data career space. In this program, you will be building technical abilities, which are necessary before pursuing opportunities in data analytics. Job postings will include a list of the required technical skills prominently displayed. Quite often, organizations will also make note of additional skills and traits that go beyond working with data on a computer. In this reading, you will explore examples of additional skills and traits that employers are seeking when searching for data analytics professionals.

As you begin to search for job opportunities, many employers seek additional skills that are not exclusive to digital fields. In the sections below, you will learn more about these traits through excerpts found within data analytics job postings.

Being coachable
Coachable individuals are capable of receiving feedback and using that information to make improvements. At the center of being coachable is a positive attitude, and the ability to self-reflect and take steps to grow. People who are coachable usually have a growth mindset, which is a belief that hard work and determination can make them better. As a result, they view feedback from colleagues and supervisors as an opportunity to improve their skill set. 

A passion for data analysis
Employers often seek candidates whose commitment to data analysis extends beyond their professional duties. Volunteering your data skills to help a nonprofit organization is just one example, but it’s not the only way to show your commitment to data analytics. Data analysis is applicable outside of the workplace, but is often not obvious. Community projects, helping a local school organize data, and developing your own side project are a few examples of how you might demonstrate your passion for data analysis outside of the workplace. 

Another way to explore your passion for data analysis is to connect with other passionate data analysts and take on data challenges. A great website for exploring data analytics is 
Kaggle.com
, which hosts an active online community for data scientists and machine learning enthusiasts. Users can collaborate with other users, publish datasets, use GPU-integrated notebooks, and compete with other data scientists to solve data science challenges. Participating in activities hosted by an online data science community like Kaggle can add fuel to your passion for data analysis and provide artifacts for your portfolio.

Employers are seeking passionate candidates. Job postings that identify candidates that have a passion for data analysis often include language like: 

Seeking a passionate data scientist.

We seek a candidate with deep curiosity directed toward diverse research interests.

Driven. The prospect of focusing on corporate environmental impact as a data domain excites you, and you are personally motivated.

Pro tip: Include in your portfolio your passion for data analysis. Provide examples of passion projects, volunteer work, or analysis outside of employment to relay your commitment to data analysis. If you are conducting data analysis in your free time, it says a lot about your passion.

Lifelong learning
Earlier, you read about some generational shifts in data workspaces. You may recall that these shifts have included technological advances in computer interfaces, data storage, and the role of data analysis in organizations.  In fact, the one consistent element through all of these eras was change. As you progress in your career, you will continue to learn new techniques, tools, and ways of interacting with other professionals. As you have learned, staying up to date with the latest technologies and techniques is essential for data professionals. Here are samples from actual job posting:

Candidates must balance their hands-on work with a desire to keep up with trends.

Seeking candidates with the aptitude and enthusiasm to develop new skills and areas of expertise.

Wanting those with a drive to learn and master new technologies and techniques.

Pro tip: Stay current by reading data-related blogs and attending workshops. Visit business networking websites to connect and learn from subject matter experts. 

Strong interpersonal skills
You may recall that data professionals and business intelligence professionals interact closely; together, they influence an organization's decision-making. Throughout a project, a data analyst will interact with many stakeholders, from different areas of an organization. Often, these interactions will extend to include clients, users, or representatives from other companies. Additionally, data professionals will work closely with a variety of other professionals as a member of a cross-functional team. Examples from job postings include interpersonal skill requirements for candidates and will include these types of statements:

We seek employees that can build relationships internally to transfer knowledge, consult with fellow data scientists and analysts to guide analysis, and deliver larger projects.

Wanting to hire those with the ability to negotiate complex and/or sensitive issues; and maintain good working relations.

Our data professionals value building strong relationships with colleagues and partners.

Pro tip: Include interpersonal skills on your list of qualifications on your resume. Revisit the material within this program (including the material on communication you will be introduced to later in this course). Find opportunities to add examples of teamwork, empathy, leadership, mediating, and active listening.

Communication
Professionals working in data, business, and technology are expected to be fluent communicators. The effectiveness of your work will weigh heavily on your ability to inspire people through the results of your analysis. Job postings can contain requirements like the following:

Candidates must have excellent communication skills and a friendly, approachable personality.

You’re a compelling storyteller who can communicate in succinct and inspiring ways to audiences with varied data science experience to influence real world product or feature decisions.

A storyteller. You know that no data speaks for itself and take pride in the visualizations and narrative that you construct to communicate your careful analysis.

Pro tip: Be sure that all correspondences you exchange are professional and free of grammatical and spelling errors. Include any examples of written communication in your portfolio–these may include but are not limited to reflection pieces, executive summaries, or  project proposals. Don’t forget to include writing examples or online blog entries that describe how you have communicated in past situations.

Problem solver
The ability to resolve problems is an important part of being a data analytics professional, whether it's dealing with incomplete data, resolving issues within an analysis, or finding the best way to communicate your results to your audience. It is important that you also quantify the results of problems that you have solved. Organizations who seek data professionals have included problem-solving in their job descriptions, such as:

Seeking data professionals who have the capability and the strong desire to solve problems from concept development, customer engagement, and technology transition.

Candidates must be persistent and have excellent analytical and problem-solving skills.

A big thinker. You start with "why" when approaching a new problem, and are always wanting to generalize, synthesize, and summarize.

Pro tip: Adjust your past working responsibilities into tasks that you were able to achieve. This can be accomplished by stressing the end results of your actions. Detail how your action or task had a direct impact on the organization. If there is a measured or empirical amount of change associated with your actions, be sure to include that. Example: Analyzed data from over 3,000 users to optimize systems, which led to a 32% increase in customer satisfaction.

Key takeaways
Employers in a variety of industries are eager to hire data professionals who possess more than just technical expertise. You can highlight your non-technical abilities for potential employers in several ways. Discover ways to successfully and effectively communicate your passions. By demonstrating a commitment to professional growth and sharing examples of your ability to solve problems, you can set yourself apart from other candidates.