Applied Data Science

AUGUST 2, 2021

They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data.

Data Science 130

Data Science Data Scientist ML ML 130

Pickl AI

OCTOBER 15, 2024

Hypothesis Testing : Statistical Models help test hypotheses by analysing relationships between variables. These models help in hypothesis testing and determining the relationships between variables. Bayesian models and hypothesis tests (like t-tests or chi-square tests) are examples of inferential models.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

Pickl AI

NOVEMBER 28, 2024

Key programming languages include Python and R, while mathematical concepts like linear algebra and calculus are crucial for model optimisation. Key Takeaways Strong programming skills in Python and R are vital for Machine Learning Engineers. According to Emergen Research, the global Python market is set to reach USD 100.6

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Becoming Human

MAY 15, 2023

One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases. Python is a High-level, Procedural, and object-oriented language; it is also a vast language itself, and covering the whole of Python is one the worst mistakes we can make in the data science journey.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

APRIL 3, 2025

Modeling & Algorithms: Applying statistical models (like regression, classification, clustering) or Machine Learning algorithms to identify deeper patterns, make predictions, or classify data points. Pattern & Trend Spotting: Makes it easier to identify relationships, trends over time, clusters, and anomalies.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Pickl AI

FEBRUARY 12, 2024

Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

JULY 25, 2023

With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. These models may include regression, classification, clustering, and more.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

MARCH 20, 2024

Proficiency in probability distributions, hypothesis testing, and statistical modelling enables Data Scientists to derive actionable insights from data with confidence and precision. Proficiency in programming languages Fluency in programming languages such as Python, R, and SQL is indispensable for Data Scientists.

Data Science 52

Data Science Data Scientist Data Wrangling Machine Learning 52

Pickl AI

MAY 29, 2024

Programming Languages (Python, R, SQL) Proficiency in programming languages is crucial. Python and R are popular due to their extensive libraries and ease of use. Python excels in general-purpose programming and Machine Learning , while R is highly effective for statistical analysis.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

OCTOBER 14, 2024

Clustering: Grouping similar data points to identify segments within the data. Techniques Hypothesis Testing: Determining whether enough evidence supports a specific claim or hypothesis. Techniques like mean, median, standard deviation, and hypothesis testing are crucial for identifying patterns and trends in data.

Data Analysis 52

Data Analysis Data Analysis EDA Data Mining 52

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. Apache Spark A fast, in-memory data processing engine that provides support for various programming languages, including Python, Java, and Scala.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

DECEMBER 4, 2024

Concepts such as probability distributions, hypothesis testing, and regression analysis are fundamental for interpreting data accurately. Programming Skills Proficiency in programming languages like Python and R is crucial for data manipulation and analysis.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 5, 2024

Programming Skills Proficiency in programming languages like Python and R is essential for Data Science professionals. Statistical Knowledge A solid understanding of statistics is fundamental for analysing data distributions and conducting hypothesis testing. These languages offer powerful libraries and frameworks.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Pickl AI

JULY 28, 2023

Tools like Python (matplotlib, seaborn) or R (ggplot2) can be helpful for creating visualizations. Python, R, SQL), any libraries or frameworks, and data manipulation techniques employed. For Data Analysts, having programming skills in languages like Python or R is increasingly valuable.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Data Visualization 52

Pickl AI

MAY 30, 2024

Also Read: Explore data effortlessly with Python Libraries for (Partial) EDA: Unleashing the Power of Data Exploration. Must Check Out: How to Use ChatGPT APIs in Python: A Comprehensive Guide. Techniques like regression analysis, hypothesis testing , and clustering help uncover patterns and relationships within the data.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

APRIL 26, 2024

Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. I devised a data cleaning and transformation strategy using Python scripts to standardise the data, which resolved the issue and improved the accuracy of the analysis.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 23, 2024

Programming Languages Python, due to its simplicity and extensive libraries, Pytho n is the most popular language in AI and Data Science. Hypothesis testing and regression analysis are crucial for making predictions and understanding data relationships. It is widely used for scripting, data manipulation, and Machine Learning.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. What is the p-value and what does it indicate in the Null Hypothesis? Is Python and SQL enough for data science?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 6, 2023

By visualizing data distributions, scatter plots, or heatmaps, data scientists can quickly identify outliers, clusters, or trends that might go unnoticed in raw data. By enabling users to interact with visual representations, Data Scientists can encourage deeper analysis, hypothesis testing, and knowledge discovery.

Data Visualization 52

Data Visualization Data Science Data Scientist Data Analysis 52