Suggested Certification for Data Scientist

Senior Data Scientist (SDS) and Professional Data Scientist (PDS) - dasca.org
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Interview Questions and Answers

Communicate insights using clear and concise language, avoid technical jargon, use visualizations to illustrate findings, and focus on the business implications and actionable recommendations.

Ethical considerations include data privacy, bias in algorithms, transparency, accountability, and fairness. Its important to ensure that data analysis and models are used responsibly and do not perpetuate discrimination or harm individuals or groups.

Common roles include Data Scientists, Data Engineers (responsible for data infrastructure), Machine Learning Engineers (focus on deploying and scaling ML models), and Data Analysts (focus on reporting and descriptive analysis).

Start by learning programming (Python), statistics, and basic machine learning concepts. Take online courses, work on personal projects, and participate in Kaggle competitions. Building a portfolio is crucial.

Challenges include dealing with messy or incomplete data, choosing the right algorithms, interpreting model results, communicating insights effectively, and staying up-to-date with the latest technologies and techniques.

Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. It can be prevented by using techniques like cross-validation, regularization, early stopping, and increasing the size of the training dataset.

Popular tools include Matplotlib, Seaborn, Plotly (Python), and ggplot2 (R) for creating static and interactive visualizations.

A/B testing is a method of comparing two versions of a webpage, app, or other digital asset to determine which one performs better. Data Scientists use statistical analysis to determine if the difference in performance is statistically significant.

Cross-validation is a technique used to assess the performance of a machine learning model on unseen data by partitioning the data into multiple folds and iteratively training and testing the model on different combinations of folds.

Supervised learning uses labeled data to train a model to predict or classify new data points. Unsupervised learning uses unlabeled data to discover patterns, clusters, or relationships within the data.

The bias-variance tradeoff refers to the balance between a models ability to accurately predict the training data (low bias) and its ability to generalize to unseen data (low variance). High bias models are underfit, while high variance models are overfit.

Python and R are the most popular languages. Python is often favored due to its versatility and extensive libraries like NumPy, Pandas, Scikit-learn, and TensorFlow. R is strong for statistical computing and visualization.

AI is the broad concept of machines performing tasks that typically require human intelligence. Machine Learning is a subset of AI that focuses on algorithms that learn from data. Data Science is a broader field that encompasses data collection, cleaning, analysis, and interpretation, and it often utilizes machine learning techniques.

EDA is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It helps to uncover patterns, spot anomalies, test hypotheses, and check assumptions before formal modeling.

Common algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, and neural networks.

Data cleaning is crucial because real-world data is often incomplete, inconsistent, and noisy. Clean data ensures the accuracy and reliability of analysis and models.

Strategies for handling missing data include imputation (replacing missing values with estimates like mean, median, or mode), deletion (removing rows or columns with missing values), and using algorithms that can handle missing data natively.

Feature engineering is the process of selecting, transforming, and creating new features from raw data to improve the performance of machine learning models. It involves domain knowledge and creativity.

A Data Scientist is a professional who uses statistical methods, machine learning algorithms, and data visualization techniques to analyze large datasets, extract meaningful insights, and help organizations make data-driven decisions.

Key skills include statistical analysis, machine learning, programming (Python, R), data visualization, database management (SQL, NoSQL), communication, and critical thinking.

A data scientist uses dynamic techniques like Machine Learning to gain insights about the future.

RStudio, Python, BI Tools, Jupyter, BigML, Domino Data Lab, SQL Consoles, MATLAB.

Data profiling, data visualizations, syntax error, normalization, handling null values, removing irrelevant data, duplicates.

In probability theory, a normal distribution is a type of continuous probability distribution for a real-valued random variable. The general form of it's probability density function is The parameter is the mean or expectation of the distribution; and is

(1)Eigenvector and Eigenvalues - Eigenvectors make linear transformations understand easily. They are the axes along which a linear transformation acts simply by stretching/compressing and/or flipping; eigenvalues give you the factors by which this compre

The statistical power of an A/B test refers to the test sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (a) if a true effect of a certain magnitude (ME

The alternative hypothesis is a position that states something is happening, a new theory is preferred instead of an old one. It is usually consistent with the research hypothesis because it is constructed from literature review, previous studies, etc.

Normality: tests for normal distribution in a population sample. T-test: tests for a Students t-distribution–ie, in a normally distributed population where standard deviation in unknown and sample size is comparatively small. Paired t-tests compare two sa

Take into considerations factors such as busiest months, big crowds, working hours etc..

Data cleansing or cleaning is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modif

The logistic model is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat,

 R Programming, Tableau Public, SAS, Apache Spark, Excel, RapidMiner, KNIME, QlikView.

Difference between Data Analysis-Data Mining: Also known as Knowledge discovery in databases-Data analysis can be divided into descriptive statistics,  exploratory data analysis, and confirmatory data analysis; It is the process of extracting important pa

The importance of data in decision lies in consistency and continual growth. It enables companies to create new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights.

 The missing data pattern is said to be monotone if the variables Yj can be ordered in such a way that if Yj is missing then all variables Yk with k > j are also missing. This occurs, for example, in longitudinal, drop-out studies. If the pattern is not m

 Data validation is a form of data cleansing used for checking the accuracy and quality of data, performed prior to importing and processing.  Data validation ensures that your data has no blank or null values, is unique, and the range of values is consis

 Duplicates, incomplete data, inconsistent formats, accessibility, system upgrades, data purging and storage.

SQL, Excel, critical thinking, R or Python–Statistical programming, data visualization, presentation skills, machine learning.

Mean, standard deviation, regression, sample size determination, hypothesis testing.

Explain with examples and numbers

 Explain with examples and convince there were no conflicts

Python, SQL and R are the most popular programming languages.