Apa itu data analyst dan data scientist

Data science menjadi perbincangan dan trend-center bagi para penggiat teknologi di bidang statistika. Sebenarnya, data science itu apa? Melansir dari Oracle, data science merupakan ilmu yang menggabungkan berbagai bidang, termasuk statistik, metode ilmiah, kecerdasan buatan (AI), dan analisis data, untuk mengekstrak nilai dari data. Penggabungan berbagai keterampilan untuk menganalisis data yang dikumpulkan dari web, smartphone, pelanggan, sensor, dan sumber lain untuk mendapatkan wawasan yang bisa untuk di olah.

Mengapa Data Science Sangat Penting?

Ilmu ini sangat menarik saat ini. Lalu, mengapa data science sangat penting? Karena perusahaan sangat membutuhkan data science. Teknologi modern telah memungkinkan penciptaan dan penyimpanan peningkatan jumlah informasi dan volume data telah meledak. Diperkirakan bahwa 90 persen dari data di dunia diciptakan dalam dua tahun terakhir. Kebanyakan data hanya berada di database dan tidak tersentuh untuk diolah. Pengelolaan data sangat dibutuhkan agar lebih tersusun dan lebih transformatif  untuk dapat memberikan suatu keputusan bagi perusahaan. 

Data science mengungkapkan tren dan menghasilkan wawasan yang dapat digunakan bisnis untuk membuat keputusan yang lebih baik dan menciptakan produk dan layanan yang lebih inovatif. Mungkin yang paling penting, ini memungkinkan model pembelajaran mesin (ML) untuk belajar dari sejumlah besar data yang diumpankan kepada mereka, daripada terutama mengandalkan analis bisnis untuk melihat apa yang dapat mereka temukan dari data.

Data Scientist, Data Analyst dan Data Engineer

Tentu saja pekerjaan di bidang data science sangat dibutuhkan di era saat ini. Banyak perusahaan yang mencari talenta digital terkait data science. Berikut beberapa role pekerjaan di bidang data science, diantaranya:

1. Data Scientist

Seorang data scientist menganalisis dan menafsirkan data digital yang kompleks untuk membantu para pemimpin bisnis membuat keputusan yang lebih baik berdasarkan data. Data scientist memiliki pengetahuan dan keahlian yang mendalam dalam matematika (aljabar linier dan kalkulus multivariabel) yang telah mereka peroleh dengan mendapatkan gelar dalam disiplin ilmu pengetahuan.

Berikut role dari data scientist, diantaranya:

• Membersihkan dan mengumpulkan data berkualitas untuk melatih algoritma
• Mengidentifikasi pola tersembunyi dalam kumpulan data
• Membangun model pembelajaran mesin
• Visualisasi data 
• Menyempurnakan metrik bisnis dengan mengembangkan dan menguji hipotesis

2. Data Analyst

Apa itu analis data? Data analyst adalah menguraikan angka dan menerjemahkannya menjadi kata-kata untuk menjelaskan apa yang dikatakan data. Mendapatkan pekerjaan analis data tidak memerlukan latar belakang matematika yang kuat. Namun, mereka tidak dapat berjalan dengan baik dalam peran ini tanpa pemahaman dalam statistik, pre-processing, visualisasi data dan analisis EDA, dan tentu saja, kemahiran dalam Excel. 

• Mengumpulkan data berdasarkan permintaan tertentu dari perusahaan.
• Membiasakan diri dengan parameter kumpulan data (jenis data, bagaimana hal itu dapat diurutkan).
• Pre-processing: memastikan data bebas dari kesalahan.
• Menafsirkan data dan menganalisis cara-cara memecahkan masalah bisnis.
• Menarik kesimpulan dari analisis.
• Memvisualisasikan dan mempresentasikan temuan kepada manajer.

3. Data Engineer

Data engineer bertanggung jawab untuk membangun, menguji dan memelihara arsitektur data. Tujuannya adalah untuk membangun dan mengoptimalkan sistem perusahaan yang memungkinkan bagi data analyst dan data scientist menyelesaikan pekerjaan mereka. Kamu harus memiliki keahlian di bidang programming, big data, dan matematika.

Selain itu, arsitektur data yang disiapkan oleh data engineer membuat dasar untuk penggunaan data lebih lanjut, termasuk: 

• Penyerapan dan penyimpanan data.
• Pembuatan algoritma.
• Penyebaran model dan algoritma machine learning.
• Visualisasi data.

Nah, gimana nih sudah tertarik bekerja di bidang data science? Role apa yang akan kamu ambil?

Referensi:

What is Data Science? | Oracle

Data Engineer vs. Data Scientist vs. Data Analyst | NCube

If you’re interested in a career working with big data and crunching numbers, there are two paths you may want to consider—becoming a data analyst or a data scientist. What is the difference between data analysts and data scientists? We’ll take a look at the differences and the career paths for both disciplines.

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Master of Science in Analytics

American University’s online MS in Analytics program prepares students to apply data analysis skills to real-world business practices. The program can be completed in 12 months. No GMAT/GRE required. 

• No GMAT or GRE scores required to apply
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Employers are looking for professionals with data-driven skills such as analytics, machine learning and artificial intelligence. As the world relies increasingly on data in many aspects of business, research and the economy, both data scientists and analysts are in demand with salaries typically above the national average.

What Does a Data Analyst Do?

A data analyst typically gathers data to identify trends that help business leaders make strategic decisions. The discipline is focused on performing statistical analyses to help answer questions and solve problems. A data analyst uses tools such as SQL to make queries to relational databases. A data analyst may also clean data, or put it in a usable format, discarding irrelevant or unusable information or figuring out how to deal with missing data.

A data analyst typically works as part of an interdisciplinary team to determine the organization’s goals and then manage the process of mining, cleaning and analyzing the data. The data analyst uses programming languages like R and SAS, visualization tools like Power BI and Tableau, and communication skills to develop and convey their findings.

What Does a Data Scientist Do?

A data scientist will typically be more involved with designing data modeling processes, creating algorithms and predictive models. Therefore, data scientists may spend more time designing tools, automation systems and data frameworks.

Compared to a data analyst, a data scientist may be more focused on developing new tools and methods to extract the information the organization requires to solve complex problems. It’s also beneficial to possess business intuition and critical-thinking skills to understand the implications of the data. Some in the field might describe a data scientist as someone who not only has mathematical and statistical knowledge but also the skills of a hacker to approach problems in innovative ways.

Differences and Similarities Between Data Analyst and Data Scientist

Both career paths require at least a bachelor’s degree in a quantitative field such as mathematics, computer science or statistics.

A data analyst may spend more time on routine analysis, providing reports regularly. A data scientist may design the way data is stored, manipulated and analyzed. Simply put, a data analyst makes sense out of existing data, whereas a data scientist works on new ways of capturing and analyzing data to be used by the analysts.

If you love numbers and statistics as well as computer programming, either path could be a good fit for your career goals. An analyst typically works on answering specific questions about the organization’s business. A data scientist may work at a more macro level to develop new ways of asking and answering important questions.

Although each role is focused on analyzing data to gain actionable insights for their organization, they’re sometimes defined by the tools they use. It helps data analysts to be proficient with relational database software, business intelligence programs and statistical software. Data scientists tend to use Python, Java and machine learning to manipulate and analyze data.

Data Analyst vs. Data Scientist: Education and Work Experience

To become a data analyst or data scientist, it may benefit you to obtain at least a bachelor’s degree in a quantitative field such as mathematics, statistics or computer science. But some analysts may have a bachelor’s in business with a focus or concentration in analytics.

Education: According to an O*NET OnLine report, 76% of business intelligence analysts have a bachelor’s degree and 14% hold a master’s degree. Data scientists and predictive analytics professionals (PAPs) are more likely to have an advanced degree. According to a Burtch Works study of salaries of data scientists and predictive analytics professionals [PDF, 1 MB] released in August 2020, at least 94% of data scientists who participated in the study held a master’s or doctorate and 86% of PAPs had a master’s or doctorate. The study also found that salaries of  professionals with advanced degrees tended to be higher than for those with only a bachelor’s degree.

Work Experience: Data science bootcamps and master’s programs in data science can allow professionals to move their careers in a different direction. There may be higher demand for professionals with work experience. Burtch Works found that 42% of the data scientists and 37% of PAPs they sampled had zero to five years of experience.

Data Analyst vs. Data Scientist: Roles and Responsibilities

A data analyst or data scientist’s role and responsibilities may vary depending on the industry and location where they work. A data analyst’s day may involve figuring out how or why something happened—such as why sales dropped—or creating dashboards that support KPIs. Data scientists, on the other hand, are more concerned with what will or could happen, using data modeling techniques and big data frameworks such as Spark.

It may be helpful to read job descriptions carefully so you have a better understanding of a company’s expectations. In some cases, job postings for data scientists may actually involve the responsibilities of a data analyst and vice versa. To get a better idea of the differences between data analysts and data scientists, here are some of the common job responsibilities of data analysts and data scientists.

Data Analysts:

• Data querying using SQL.
• Data analysis and forecasting using Excel.
• Creating dashboards using business intelligence software.
• Performing various types of analytics including descriptive, diagnostic, predictive or prescriptive analytics.

Data Scientists:

• A data scientist may spend up to 60% of their time scrubbing data.
• Data mining using APIs or building ETL pipelines.
• Data cleaning using programming languages (e.g. Python or R).
• Statistical analysis using machine learning algorithms such as natural language processing, logistic regression, kNN, Random Forest or gradient boosting.
• Creating programming and automation techniques, such as libraries, that simplify day-to-day processes using tools like Tensorflow to develop and train machine learning models.
• Developing big data infrastructures using Hadoop and Spark and tools such as Pig and Hive.

Each role analyzes data and gains actionable insights to make business decisions. Data analysts use SQL, business intelligence software and SAS, a statistical software, while data scientists use Python, JAVA and machine learning to make sense of data.

Data Analyst vs. Data Scientist: Skill Comparison

There is some overlap in analytics between data scientist skills and data analyst skills, but the main differences are that data scientists typically use programming languages such as Python and R, while data analysts may use SQL or Excel to query, clean or make sense of data. Another difference is the techniques or tools they use to model data: Data analysts typically use Excel and data scientists use machine learning. It’s important to note that some advanced analysts may use programming languages or have familiarity with big data.

To better understand the differences between data analysts and data scientists, here are some of the common job skills of data analysts and data scientists.Data Analysts vs Data Scientists

Data Analyst vs. Data Scientist: Job Outlook

A data analyst or data scientist’s salary may vary depending on their industry and employer. The job outlook for data scientists is bright and the projected growth between . According to O*NET, data analysts may earn a median annual salary of $98,230.

Master of Science in Applied Data Science

Syracuse University’s online Master of Science in Data Science can be completed in as few as 18 months.

• Complete in as little as 18 months
• No GRE scores required to apply

University of California, Berkeley

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Master of Information and Data Science

Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.

• Complete in as few as 12 months
• No GRE required

Southern Methodist University

info

Master of Science in Data Science

Earn your MS in Data Science at SMU, where you can specialize in Machine Learning or Business Analytics, and complete in as few as 20 months.

• No GRE required.
• Complete in as little as 20 months.

Data Analytics vs. Data Science: How the Two Careers Are Different

In addition to computer science, some data scientists may choose to apply their skills to specific areas of interest to them, such as engineering and natural sciences. To advance their careers, they can dig deeper with an online master’s in data science program.

The data scientist route focuses on learning frameworks for processing, analyzing, modeling and drawing conclusions from data. A data scientist might use a data lake to manage unstructured data for analysis.

A data analyst might pursue knowledge to use statistics, analytics technology and business intelligence to answer specific questions for the organization.

In addition to technical skills, data analysts and data scientists may benefit from soft skills to work in teams and communicate their findings. They should understand their organization’s priorities and nuances and apply critical thinking and business intuition to communicate their process and findings.

Career Growth

A data analyst may start out in an entry-level role where their main responsibilities are reporting and creating dashboards. The next step may be to take on a role that involves strategy or advanced analytics techniques. Taking it a step further, an advanced analyst may be interested in a managerial role and become an analytics manager after working for over nine years. In some cases, a data analyst may continue their education and sharpen their skills to become a data scientist.

There is currently a skills gap in data science, with many more open positions than there are skilled professionals to fill them. Companies seeking to fill these roles are looking to career-changers who have completed bootcamps, as well as training their current employees. Someone currently working as a data scientist may choose to continue their education and earn a doctorate to position themselves for more advanced data science roles.

Other Related Careers

For more information on careers in data science, read these helpful guides:

• Tech Bootcamp Guide
• Data Analytics Bootcamp Guide
• Coding Bootcamp Guide
• Online courses
• Learn more

Data Analyst vs Data Scientist FAQ

Is data science or data analytics a better degree?

The best degree for you depends on your personal and professional goals. If you’re interested in data processing and statistical modeling, a degree in data analytics may be right for you. If you’re interested in machine learning or big data, you may want to pursue a degree in data science.

Can a data analyst become a data scientist?

There is some overlap between the role of a data analyst and a data scientist which may help a data analyst transition into a data scientist. Everyone’s path is different, but a common step is typically gaining relevant data science skills and continuing education.

What are the common skills used by data analysts and data scientists?

Common skills used by both data analysts and data scientists may include data mining, data warehousing, math, statistics and data visualization. Depending on their role in an organization, some data analysts may use programming languages such as R or Python.

Last updated: April 2022

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