Juana  Wunsch

Juana Wunsch


Top 8 Skills for Every Data Scientist

As I attend university talks, the most common question I get asked is “What skills do I need to have?”

Answering the question “what skills do I need to have” can come with various answers depending on who you are talking to, what company you are looking at, and even what job position you are applying to. After many talks with college students on this topic, I wanted to sit down and discuss how I look at this question and the top 8 areas to consider when looking at skills.

  1. Developing a Business Problem
  2. Working with Big and Small Data Ingestion and Processing
  3. Understanding Data Cleaning and Preprocessing
  4. Working with Tools
  5. Drafting Visualizations, Reports, and Dashboards
  6. Understanding the Analytics Life Cycle
  7. Analytics to Production
  8. Embracing Research and Development (R&D)

1. Developing a Business Problem

As you look at what skills you need to work in data science, one key area is learning to develop or understand the business problem you are trying to solve. It is essential to understand the business justification for the work you will be doing and how the customer will utilize it. Often, we can get caught up in a cool idea, but we miss the business aspect. If no customer is asking for the work, no reason to run the analyses, then what are you doing? Understand how this work will be used and provide value to the customer. Developing skills in this area to understand business justification and value-add can help you continue to build data science projects and present your work to others.

2. Working with Big and Small Data Ingestion and Processing

As you look for a job in data, you will need to know how to ingest and process the data you are working on. The skill sets in this area may vary. Suppose you are looking more towards data engineering. In that case, you may find yourself developing databases, creating relationships between the data sources, and making data marts for people to come to get the data from you. Your skills need to be in how to create and maintain those data sources. If this is the case for you, focus on knowing different database types, how to use those databases, and how to create relationships within the data.

If you are looking to find a data science or data analyst job, you may be more focused on how to bring that data into your workspace. Do you need to connect to a database or use an API? Are you developing code that will interact with this data or software tools like Power BI and Tableau? Your skills in this area may vary depending on the type of role you are applying to. Still, it is good to have at least a basic understanding of how to interact with different data sources and ingest that data into your tools or environments. Knowledge of how this data gets ingested before you start your analyses is essential.

3. Understanding Data Cleaning and Preprocessing

Data cleaning and processing will also vary between jobs. In the first case of being a data engineer and creating the data sources, your cleaning and preprocessing may be more generic to the overall ingestion process. You may want to remove duplicate entries, clean up how the data sources are interconnected, and create a usable database that others can work with. In this position, you are not as focused on intense data cleaning techniques.

Now, what I mean by that is, if you are a scientist or analyst looking at the data, you may ingest a data engineers dataset and still need to clean and preprocess the data to work with what you are doing. You need skills in one-hot encoding, cleaning and handle text data, imputation, and making sure the data columns are the expected data types for what you are doing. Understanding different ways data cleaning and preprocessing happens and implementing them depending on your end use-case are valuable skills that you will often need as you work with the data. You should understand the main concepts of data cleaning and preprocessing relative to the job you are looking for.

#data-science #machine-learning #artificial-intelligence #developer #python

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Top 8 Skills for Every Data Scientist
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Java Questions

Java Questions


50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india

Sid  Schuppe

Sid Schuppe


Data Analyst vs. Data Scientist

Stylised as the sexiest job of the 21st century, data science has emerged as one of the most in-demand professions of recent years — taking hold with a hype that normally only surrounds celebrities. Companies worldwide put lucrative salaries, prestige and the privilege of wielding influence up for grabs to attract analytical talent. Behind all the hype is a growing importance of digital data that’s currently transforming the way we live and work.
It’s no wonder that more and more enthusiasts want to break into this new field. But before venturing into data science and analytics with one’s eyes closed, aspirants are well advised to inform themselves about available routes first. Interested candidates are encouraged to begin their journey by identifying entry points and requirements, by finding out more about how the various data subfields differ from one another, and how their CV needs refinement prior to submitting job applications.

#data-analyst-jobs #data-scientist #data-analyst #data-scientist-skills #data-science

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Ian  Robinson

Ian Robinson


Data Science: Advice for Aspiring Data Scientists | Experfy Insights

Around once a month, I get emailed by a student of some type asking how to get into Data Science, I’ve answered it enough that I decided to write it out here so I can link people to it. So if you’re one of those students, welcome!

I’ll segment this into basic advice, which can be found quite easily if you just google ‘how to get into data science’ and advice that is less common, but advice that I’ve found very useful over the years. I’ll start with the latter, and move on to basic advice. Obviously take this with a grain of salt as all advice comes with a bit of survivorship bias.

Less Basic Advice:

1. Find a solid community

2. Apply Data Science to Things you Enjoy

3. Minimize the ‘Clicks to Proof of Competence’

4. Learn Through Research or Entry Level Jobs

#big data & cloud #data science #data scientist #statistics #aspiring data scientist #advice for aspiring data scientists