Data Science Dojo

AUGUST 30, 2023

Statistical analysis and hypothesis testing Statistical methods provide powerful tools for understanding data. An Applied Data Scientist must have a solid understanding of statistics to interpret data correctly.

Data Science 195

Data Science Hypothesis Testing Machine Learning Machine Learning 195

Pickl AI

AUGUST 9, 2024

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of Big Data Understanding the fundamentals of Big Data is crucial for anyone entering this field.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 10, 2023

As a programming language it provides objects, operators and functions allowing you to explore, model and visualise data. The programming language can handle Big Data and perform effective data analysis and statistical modelling. Suppose you want to develop a classification model to predict customer churn.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

MARCH 20, 2024

Essential technical skills Understanding of statistics and probability A strong foundation in statistics and probability theory forms the bedrock of Data Science. Mastery of statistical concepts equips professionals to make informed decisions and draw accurate conclusions from empirical observations.

Data Science 52

Data Science Data Scientist Data Wrangling Data Visualization 52

Pickl AI

FEBRUARY 12, 2024

Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesis testing, regression analysis, and experimental design, is paramount in Data Science roles. It forms the basis for many statistical tests and estimators used in hypothesis testing and confidence interval estimation.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

Concepts such as probability distributions, hypothesis testing , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. Decision Trees These trees split data into branches based on feature values, providing clear decision rules.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

APRIL 28, 2023

Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability Big Data: (..)

Data Scientist 52

Data Scientist Data Science Hypothesis Testing Decision Trees 52