IBM Journey to AI blog

JULY 6, 2023

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

IBM Journey to AI blog

DECEMBER 19, 2023

Supervised learning Supervised learning techniques use real-world input and output data to detect anomalies. These types of anomaly detection systems require a data analyst to label data points as either normal or abnormal to be used as training data.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

ODSC - Open Data Science

FEBRUARY 5, 2025

Ozdemir focused on structured agent workflows , breaking down AI tasks into decision trees with modular components. He discussed autonomous agents using rule-based and machine learning-driven approaches , highlighting the importance of self-reflection mechanisms in AI workflows.

AI 52

AI AI Decision Trees Machine Learning 52

Mlearning.ai

MAY 23, 2023

What is the difference between data analytics and data science? Data science involves the task of transforming data by using various technical analysis methods to extract meaningful insights using which a data analyst can apply to their business scenarios. Decision trees are more prone to overfitting.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

JANUARY 29, 2024

Key Components In Data Science, key components include data cleaning, Exploratory Data Analysis, and model building using statistical techniques. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. billion in 2023 to an impressive $225.91

Data Science 52

Data Science ML ML Machine Learning 52

Mlearning.ai

APRIL 17, 2023

It is similar to the random forest in that it combines multiple decision trees to create a strong learner. It iteratively builds a sequence of decision trees, where each tree is trained to correct the errors made by the previous trees in the sequence. BECOME a WRITER at MLearning.ai

Decision Trees 52

Decision Trees Algorithm Data Analyst Python 52

Pickl AI

SEPTEMBER 23, 2024

Hypothesis testing and regression analysis are crucial for making predictions and understanding data relationships. Machine Learning Supervised Learning includes algorithms like linear regression, decision trees, and support vector machines. They also optimise algorithms to ensure robust performance in real-world applications.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Pickl AI

APRIL 3, 2025

Modeling: Build a logistic regression or decision tree model to predict the likelihood of a customer churning based on various factors. Tools Commonly Used Business Intelligence Platforms: Tableau, Microsoft Power BI, Qlik Sense, Google Data Studio (Looker Studio) Programming Libraries: Matplotlib, Seaborn (Python); ggplot2 (R); D3.js

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Pickl AI

FEBRUARY 4, 2024

Automation not only saves time but also enhances accuracy and consistency by minimizing manual intervention, thereby contributing to more reliable insights and better decision-making. From linear regression to decision trees, Alteryx provides robust statistical models for forecasting trends and making informed decisions.

Data Preparation 52

Data Preparation Tableau Data Visualization SQL 52

Pickl AI

NOVEMBER 2, 2023

linear regression, decision trees , SVM) – Understanding about the perfect fit for using each algorithm – Parameters and hyperparameters to tune Click here to access -> Cheat sheet for Key Machine Learning Algorithms Deep Learning Concepts and Neural Network Architectures – Neural network components and their functions (e.g.,

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Pickl AI

OCTOBER 18, 2023

How it Works Random Forest creates a “forest” of decision trees and combines their outputs to achieve more stable and accurate predictions. Random Forest: A Versatile Machine Learning Algorithm Random Forest is a flexible and widely machine-learning algorithm known for its simplicity and reliability.

Data Quality 52

Data Quality ML ML Machine Learning 52

Pickl AI

JULY 18, 2023

Data Security: SQL supports user authentication and authorization. Thus allowing database administrators to control access to data and grant specific privileges to users or user groups. Read Blog Advanced SQL Tips and Tricks for Data Analysts 4. SAS provides a wide range of statistical procedures and algorithms.

Data Science 52

Data Science SQL Data Scientist Python 52

Pickl AI

JULY 23, 2023

Data Interpretation Interpreting the results of data analysis is essential for drawing meaningful conclusions and making data-driven decisions. Accurate interpretation hinges on the expertise of data analysts and domain experts. Key Features: i.

Analytics 52

Analytics Analytics Predictive Analytics Data Analysis 52

IBM Journey to AI blog

JUNE 5, 2024

Using comprehensive, AI-driven SaaS analytics, businesses can make data-driven decisions about feature enhancements, UI/UX improvements and marketing strategies to maximize user engagement and meet—or exceed—business goals. They may also struggle to fully leverage the predictive capabilities of app analytics.

Analytics 94

Analytics Analytics AI AI 94

Dataconomy

MARCH 28, 2025

By systematically narrowing down the vast array of potential features, data analysts can enhance the model’s focus on the most informative elements. This not only optimizes accuracy but also improves efficiency, which is particularly important in todays data-driven world.

Data Analyst 91

Data Analyst Decision Trees Machine Learning Machine Learning 91

Pickl AI

DECEMBER 4, 2024

Furthermore, the demand for skilled data professionals continues to rise; searches for “data analyst” roles have doubled in recent years as companies seek to harness the power of their data. Gain hands-on experience using frameworks such as TensorFlow or PyTorch to build and train models.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

How to Learn Machine Learning

MARCH 25, 2025

Some common supervised learning algorithms include decision trees, random forests, support vector machines, and linear regression. These algorithms help businesses make decisions when there is clear historical data available. Unsupervised learning uses algorithms that help discover groupings and associations in data.

Supervised Learning 59

Supervised Learning Machine Learning Machine Learning Algorithm 59