Data science

Softecks

ବିଜ୍ଞାପନ ଅନ୍ତର୍ଭୂକ୍ତଆପ୍‌ ମଧ୍ୟରେ କ୍ରୟଗୁଡ଼ିକ

ଏହି ଆପ୍ ବିଷୟରେ

Data science can feel challenging when Python, statistics and machine learning are scattered across resources. Data Science brings lessons and calculators together, helping you study data analysis, visualization and statistics offline for classes, projects and interviews.

📚 CORE FEATURES

✅ Structured data science lessons
Explore 12 areas: Foundations & Purpose; Careers, Roles & Languages; Industry Applications & Case Studies; Boundaries & Comparisons; Core Methods & Algorithms; Python Foundations; Data I/O & Ingestion; Wrangling & Aggregation; Visualization; Time Series, Spatial & Network; Descriptive Stats & Distributions; and Statistical Tests & Relationships.

✅ Learn Python for data analysis
Study Python data input from files, web sources and databases, then explore cleaning, aggregation, charts and visualization. Connect them with time series, geospatial and network data concepts.

✅ Build confidence with statistics
Review mean, median, mode, variance, standard deviation, percentiles, z-scores, probability distributions, correlation, covariance, hypothesis testing and confidence intervals. Learn what different measurements tell you about a dataset before drawing conclusions.

✅ 21 free data science tools
Try mean/median/mode, variance, standard deviation, range and IQR, percentile, z-score, weighted mean, covariance, correlation, probability, normal and binomial distributions, sample size, confidence intervals, normalization, min-max scaling, standardization, missing values, outlier detection, CSV summaries and descriptive statistics.

✅ Expand to all 45 tools
A one-time purchase unlocks the full tool collection, adding linear and logistic regression, R-squared, MAE, MSE, RMSE, classification metrics, confusion matrix, ROC/AUC, K-means, KNN, decision trees, entropy, Gini impurity, Naive Bayes, PCA, similarity, distance, moving averages, exponential smoothing, A/B testing, feature importance and project planning.

✅ How-To, offline reading and search
Use 30 new How-To articles, offline lessons, bookmarks and search to revisit Python, machine learning, statistics and visualization topics.

👥 WHO IS THIS FOR?

• Students: revise statistics, assignments and viva questions with lessons and example calculations.
• Python beginners: learn data input, cleaning, aggregation and plots before trying code.
• Aspiring analysts: explore summary statistics, missing values and scaling as you study datasets.
• Project learners: revisit regression, clustering, model evaluation and hypothesis testing before interviews.

💡 WHY CHOOSE DATA SCIENCE?

Instead of switching between notes and isolated calculators, study concepts and related math in one app. Lessons, 30 How-To articles, 21 free tools, a one-time tool unlock, bookmarks and offline reading support revision. For example, review a CSV summary and check outliers before exploring model performance. This is an educational reference, not a live Python IDE or a substitute for professional validation.

❓ FREQUENTLY ASKED QUESTIONS

Q: Can I learn data science offline?
A: Yes. The lesson content supports offline access, helping you study while travelling or away from a reliable connection.

Q: Does the app teach Python and statistics?
A: Yes. Topics include Python data ingestion, wrangling, visualization, descriptive statistics, distributions, statistical testing and relationships.

Q: What is included without purchasing?
A: There are 21 free calculators for statistics, probability, normalization, missing data, outliers and CSV summaries. A one-time purchase unlocks all 45 tools.

Q: Can I train machine learning models here?
A: The app teaches ML concepts and provides analytical calculators. It is not a live model-training environment or Python code runner.

🚀 Download Data Science today to learn Python, statistics, data analysis, machine learning concepts and visualization. Build stronger foundations, explore useful tools and make your next study session count!

ନୂଆ କଣ ଅଛି

✅ Added new interactive tools
✅ Introduced comprehensive How-To sessions
✅ Enhanced overall performance and user interface design