I will do data science and analytics
I will do data science and analytics
One of the biggest differences between data analysts and scientists is what they do with data. Data analysts typically work with structured data to solve
Get I will do data science and analytics
Do you need help with your data? I am a data science and analytics professional with experience in various industries. I can help you with data analysis, data visualization, and predictive modeling. I will provide you with insights that can help you make better decisions.
Feel free to reach out prior to see what we can expect from each other!
You provide the data, I will provide the results.
Data science and analytics are two related fields that are often used interchangeably.
Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines1.
While data scientists are expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources1.
The major difference between data science and data analytics is scope.
A data scientist’s role is far broader than that of a data analyst, even though the two work with the same data sets1.
For that reason, a data scientist often starts their career as a data analyst1. Data analytics refers to the process and practice of analyzing data to answer questions, extract insights, and identify trends2.
Whereas data analytics is primarily focused on understanding datasets and gleaning insights that can be turned into actions, data science is centered on building, cleaning, and organizing datasets2.
There are many tools used in data science such as regression analysis, Monte Carlo simulation, factor analysis, cohort analysis, cluster analysis, time series analysis, and sentiment analysis1.
Other techniques include linear regression, logistic regression, jackknife regression, density estimation, confidence interval, test of hypotheses, pattern recognition, and clustering1.
Data can be collected through surveys, transactional tracking, interviews, focus groups, observation, online tracking, forms, and social media monitoring1.
Data analysis can involve text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis1.
Some of the most popular data science tools include Excel2, Apache Spark3, D3.js3, IBM SPSS3, R4, Python4, SAS4, Tableau4, RapidMiner4, KNIME4, Weka4, MATLAB4, and Alteryx4.
I'm an MSc Student in Artificial Intelligence, focusing mainly on Machine Learning and Data Science. Next to this, I have work experience in the ML field and am able to adapt quickly to new technologies and challenges.
-Data Cleaning -Data Manipulation -Feature Engineering
2 Days Delivery
- 30 minutes live consultation
- 2 questions answered
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