Data Science Dojo

JANUARY 31, 2023

Data types are a defining feature of big data as unstructured data needs to be cleaned and structured before it can be used for data analytics. In fact, the availability of clean data is among the top challenges facing data scientists.

Big Data 370

Big Data Big Data Data Mining Data Mining 370

Smart Data Collective

OCTOBER 17, 2022

The final point to which the data has to be eventually transferred is a destination. The destination is decided by the use case of the data pipeline. It can be used to run analytical tools and power data visualization as well. Otherwise, it can also be moved to a storage centre like a data warehouse or lake.

Data Pipeline 130

Data Pipeline Data Warehouse ETL Exploratory Data Analysis 130

Pickl AI

MARCH 12, 2023

Accordingly, Data Analysts use various tools for Data Analysis and Excel is one of the most common. Significantly, the use of Excel in Data Analysis is beneficial in keeping records of data over time and enabling data visualization effectively. How to use Excel in Data Analysis and why is it important?

Data Analysis 52

Data Analysis Data Analysis Data Analyst Power BI 52

Analytics Vidhya

APRIL 17, 2023

Are you a data enthusiast looking to break into the world of analytics? The field of data science and analytics is booming, with exciting career opportunities for those with the right skills and expertise. So, let’s […] The post Data Scientist vs Data Analyst: Which is a Better Career Option to Pursue in 2023?

Data Analyst 303

Data Analyst Data Scientist Data Science Analytics 303

Dataconomy

JULY 28, 2023

Data scientists must decide on appropriate strategies to handle missing values, such as imputation with mean or median values or removing instances with missing data. The choice of approach depends on the impact of missing data on the overall dataset and the specific analysis or model being used.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Mlearning.ai

MAY 23, 2023

It starts with gathering the business requirements and relevant data. Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. In the final stage, the results are communicated to the business in a visually appealing manner.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52