DataCamp Tracks
DataCamp tracks road map for computer science students.
Python Data Analysis Tracks
Python Data Analysis Tracks road map for computer science students, which including the following main topics:
Python Data Analysis Tracks (main topics)
1. Basic Programming Tracks 17 Entities
2. Probability and Statistics Tracks 14 Entities
3. Data Preprocessing Tracks 15 Entities
4. Data Visualization Tracks 8 Entities
5. Data Analysis Tracks 17 Entities
SKILLS YOU WILL GAIN:
object-oriented programming, databases, mongodb, data science toolbox, command line automation, aws boto, unit testing for data science, analyzing marketing campaigns, analyzing police activity, analyzing social media data, arima models, customer segmentation, market basket analysis, marketing analytics predicting customer churn, working geospatial data, supply chain analytics, analyzing us census data, python for spreadsheet users, exploratory data analysis, probability, statistics, linear modeling, network analysis, generalized linear models, practicing statistics interview questions, experimental design, customer analytics a/b testing, time series analysis, importing data, cleaning data, web scraping, data manipulation, dealing missing data, joining data, manipulating time series data, working dates times, pandas foundations, manipulating dataframes, merging dataframes, pandas joins for spreadsheet users, data visualization, matplotlib, seaborn, bokeh, geospatial data, time series data, software engineering for data scientists, parallel programming dask, portfolio analysis, portfolio risk management, importing managing financial data, quantitative risk management, financial forecasting
Python Machine Learning Tracks
Python Machine Learning Tracks road map for computer science students, which including the following main topics:
Python Machine Learning Tracks (main topics)
1. Machine Learning Tracks 24 Entities
2. Deep Learning Tracks 10 Entities
3. Natural Language Processing Tracks 9 Entities
4. Applied Finance Tracks 10 Entities
5. Data Engineering Tracks 11 Entities
SKILLS YOU WILL GAIN:
data science, feature engineering, machine learning, winning kaggle competition, working dates times, data visualization, software engineering for data scientists, preprocessing for machine learning, linear classifiers, unsupervised learning, supervised learning scikit-learn, machine learning tree-based models, predictive analytics, dimensionality reduction, designing machine learning workflows, machine learning for time series data, machine learning for marketing, human resources analytics, machine learning for finance, extreme gradient boosting xgboost, parallel programming dask, fraud detection, cluster analysis, model validation, hyperparameter tuning, ensemble methods, natural language processing, regular expressions, sentiment analysis, feature engineering for nlp, machine translation, spoken language processing, building chatbots, advanced nlp spacy, deep learning, keras, pytorch, recurrent neural networks for language modeling, predicting ctr machine learning, image processing, biomedical image analysis, credit risk modeling, python for finance financial concepts, quantitative risk management, financial forecasting, pyspark, data engineering, spark sql, big data fundamentals pyspark, feature engineering pyspark, cleaning data pyspark, machine learning pyspark, building recommendation engines pyspark, streaming data aws kinesis lambda, building data engineering pipelines
R Data Analysis Tracks
R Data Analysis Tracks road map for computer science students, which including the following main topics:
R Data Analysis Tracks (main topics)
1. Basic Programming Tracks 18 Entities
2. Probability and Statistics Tracks 16 Entities
3. Data Preprocessing Tracks 20 Entities
4. Data Visualization Tracks 17 Entities
5. Data Analysis Tracks 27 Entities
SKILLS YOU WILL GAIN:
reporting r markdown, visualizing geospatial data, joining data table, marketing analytics, garch models, survey and measurement development, single-cell rna-seq bioconductor, data manipulation dplyr, object-oriented, communicating data tidyverse, developing r packages, importing data, inference for categorical data, topic modeling, handling missing data imputations, data manipulation, statistics, working dates and times, spatial analysis sf and raster, probability, regular expressions, business process analytics, functional programming purrr, anomaly detection, parallel programming, building dashboards flexdashboard, writing functions, visualizing big data trelliscope, designing and analyzing clinical trials, fraud detection, arima models, factor analysis, choice modeling for marketing, dealing missing data, data privacy and anonymization, modeling data tidyverse, cleaning data, time series analysis, probability puzzles, statistical modeling, life insurance products valuation, data visualization, visualizing time series data, network analysis tidyverse, chip-seq bioconductor, rna-seq bioconductor, working web data, differential expression analysis limma, feature engineering, exploratory data analysis, building dashboards shinydashboard, importing and managing financial data, working data tidyverse, analyzing us census data, joining data dplyr, generalized linear models, interactive maps leaflet, multivariate probability distributions
R Machine Learning Tracks
R Machine Learning Tracks road map for computer science students, which including the following main topics:
R Machine Learning Tracks (main topics)
1. Machine Learning Tracks 27 Entities
2. Applied Finance Tracks 10 Entities
3. Data Engineering Tracks 12 Entities
SKILLS YOU WILL GAIN:
text analysis, linear algebra for data science, classification, tidyverse, regression, tree-based models, analyzing social media data, hyperparameter tuning, sentiment analysis, text mining bag-of-words, hierarchical and mixed effects models, logistic regression, equity valuation, categorical data tidyverse, r for finance, machine learning, predictive analytics using networked data, quantitative risk management, bond valuation and analysis, cluster analysis, inference for numerical data, scalable data processing, correlation and regression, inference for linear regression, bayesian data analysis, survival analysis, bayesian modeling rjags, forecasting, advanced dimensionality reduction, experimental design, forecasting product demand, financial trading, big data r, credit risk modeling, support vector machines, nonlinear modeling gams, bayesian regression modeling rstanarm, mixture models, optimizing r code rcpp, dimensionality reduction, natural language processing, human resources analytics, building web applications shiny, analyzing election and polling data, spark sparklyr, inference, a/b testing, market basket analysis, network analysis, portfolio analysis,
SQL Tracks
SQL Tracks road map for computer science students, which including the following main topics:
SQL Tracks (main topics)
1. SQL Tracks 10 Entities
2. SQL Server Tracks 11 Entities
3. PostgreSQL Tracks 5 Entities
4. Oracle SQL Tracks 2 Entities
SKILLS YOU WILL GAIN:
analyzing business data sql, sql, intermediate sql, exploratory data analysis sql, relational databases sql, joining data sql, reporting sql, applying sql real-world problems, database design, data-driven decision making sql, sql server, intermediate sql server, functions for manipulating data sql server, cleaning data sql server databases, hierarchical recursive queries sql server, time series analysis sql server, improving query performance sql server, writing functions stored procedures sql server, transactions error handling sql server, building optimizing triggers sql server, creating postgresql databases, functions for manipulating data postgresql, postgresql summary stats window functions, cleaning data postgresql databases, improving query performance postgresql, transactions error handling postgresql, oracle sql
Theory Tracks
Theory Tracks road map for computer science students, which including the following main topics:
Theory Tracks (main topics)
1. Theory Tracks 8 Entities
SKILLS YOU WILL GAIN:
data science for business, data science for everyone, machine learning for business, machine learning for everyone, data visualization for everyone, data engineering for everyone, cloud computing for everyone, data driven decision making
Spreadsheets and Excel Tracks
Spreadsheets and Excel Tracks road map for computer science students, which including the following main topics:
Spreadsheets and Excel Tracks (main topics)
1. Excel Tracks 2 Entities
2. Spreadsheets Tracks 13 Entities
SKILLS YOU WILL GAIN:
data analysis excel, data analysis spreadsheets, spreadsheets, intermediate spreadsheets, statistics spreadsheets, error uncertainty spreadsheets, conditional formatting spreadsheets, pivot tables spreadsheets, data visualization spreadsheets, loan amortization spreadsheets, marketing analytics spreadsheets, financial analytics spreadsheets, financial modeling spreadsheets, options trading spreadsheets
Tableau and Power-BI Tracks
Tableau and Power BI Tracks road map for computer science students, which including the following main topics:
Tableau and Power-BI Tracks (main topics)
1. Tableau Tracks 5 Entities
2. Power BI Tracks 2 Entities
SKILLS YOU WILL GAIN:
tableau, analyzing data tableau, power bi, creating dashboards in tableau, connecting data in tableau, statistical techniques in tableau, analyzing data in tableau
Shell and Git Tracks
Shell and Git Tracks road map for computer science students, which including the following main topics:
