LoginSign Up

Data Science Course

Master Data Science and Analytics

Build comprehensive data science expertise while mastering data analysis, machine learning, and statistical techniques. This comprehensive course combines data science fundamentals with advanced analytics and AI concepts, preparing you for real-world data challenges and industry applications. Learn to extract insights from data, build predictive models, and develop data-driven solutions required for data scientist roles.

What You Will Learn:

  • Data Science Foundations: Understand the fundamentals of data science, including the data science lifecycle, key roles and responsibilities, and essential tools and technologies used in the field.
  • Programming for Data Science: Master programming languages like Python and R, learn data manipulation with libraries like Pandas, and create compelling visualizations using Matplotlib, Seaborn, and other visualization tools.
  • Data Wrangling and Statistical Analysis: Gain proficiency in data cleaning, handling missing data, statistical analysis, hypothesis testing, and probability distributions essential for data analysis.
  • Machine Learning and AI: Learn machine learning algorithms, model evaluation, deep learning fundamentals, natural language processing, and advanced techniques for building predictive models and AI solutions.
  • Big Data and Cloud Technologies: Explore big data frameworks like Hadoop and Spark, work with cloud services (AWS, Google Cloud, Azure), and handle large-scale data processing and storage solutions.
  • Data Visualization and Communication: Master the art of storytelling with data, create impactful dashboards using tools like Tableau and Power BI, and effectively communicate insights to stakeholders.
  • Real-World Projects: Apply your skills in practical scenarios with hands-on data science projects. Develop a comprehensive capstone project that showcases your mastery of data science techniques and methodologies.

Why Choose This Course:

  • Expert Instruction: Learn from experienced data scientists and industry professionals who bring real-world experience and current industry knowledge to the classroom.
  • Hands-On Data Projects: Engage in interactive data analysis exercises and real-world data science projects that reinforce your learning and provide practical experience with actual datasets.
  • Flexible Learning: Study at your own pace with lifetime access to all course materials, including video lectures, coding exercises, datasets, and downloadable resources.
  • Comprehensive Data Science Curriculum: Cover all essential aspects of data science, from basic statistics to advanced machine learning, ensuring a well-rounded understanding of the field.
  • Data Science Community: Benefit from a supportive learning environment with access to the course forum, where you can discuss data science topics, share insights, and collaborate with fellow data science enthusiasts and professionals.
Ideal For:
  • Beginners with no prior data science experience looking to start a career in data analytics, machine learning, or data engineering.
  • Professionals from other fields who want to transition into data science and learn essential data analysis and machine learning concepts.
  • Data analysts seeking to enhance their skills in advanced analytics, machine learning, or big data technologies and AI applications.
Enroll Today:
  • Take the first step towards mastering data science. Enroll now and start your journey to becoming a skilled data scientist and analytics professional!

This comprehensive data science course will provide you with the knowledge and hands-on experience needed to extract valuable insights from data and build predictive models. Join thousands of students who have successfully launched their data science careers with our expert-led training program.

Curriculum

Overview of data science and its applications30:25🔒
The data science lifecycle (data collection, cleaning, analysis, visualization, interpretation)25:40🔒
Key roles and responsibilities of a data scientist20:15🔒
Tools and technologies used in data science35:30🔒

Introduction to programming with Python or R35:15🔒
Basic programming concepts (variables, data types, operators, control structures)28:40🔒
Functions and modules22:30🔒
Data manipulation with libraries (Pandas for Python, dplyr for R)18:25🔒
Data visualization libraries (Matplotlib, Seaborn for Python; ggplot2 for R)20:15🔒

Importance of data quality and integrity30:25🔒
Handling missing data and outliers25:40🔒
Data transformation techniques (normalization, scaling, encoding)35:15🔒
Combining and reshaping datasets28:30🔒
Using tools like Excel, SQL for data manipulation32:45🔒

Descriptive statistics (mean, median, mode, variance)35:25🔒
Data visualization techniques (histograms, scatter plots, box plots)42:40🔒
Identifying patterns, trends, and relationships in data38:30🔒
Correlation and causation28:15🔒
Summary statistics and data distributions32:45🔒

Introduction to probability and statistics40:30🔒
Probability distributions (normal, binomial, Poisson)35:20🔒
Hypothesis testing and confidence intervals38:15🔒
ANOVA and regression analysis42:45🔒
Statistical significance and p-values30:25🔒

Overview of machine learning and its types (supervised, unsupervised)42:30🔒
Key algorithms and techniques (linear regression, decision trees, k-nearest neighbors, clustering)38:45🔒
Model evaluation metrics (accuracy, precision, recall, F1 score)35:20🔒
Overfitting, underfitting, and cross-validation28:15🔒
Introduction to libraries (Scikit-learn, TensorFlow, Keras)25:30🔒

Principles of effective data visualization50:20🔒
Creating visualizations to tell a story (charts, dashboards)35:45🔒
Tools for visualization (Tableau, Power BI, Matplotlib, Seaborn)42:30🔒
Communicating insights and findings to stakeholders42:30🔒
Creating reports and presentations42:30🔒

Introduction to advanced algorithms (ensemble methods, support vector machines)50:20🔒
Deep learning fundamentals and neural networks35:45🔒
Natural Language Processing (NLP) basics42:30🔒
Model tuning and optimization42:30🔒
Handling large datasets and distributed computing42:30🔒

Introduction to big data concepts and technologies50:20🔒
Working with big data frameworks (Hadoop, Spark)35:45🔒
Data storage solutions (SQL vs. NoSQL databases)42:30🔒
Introduction to cloud services (AWS, Google Cloud, Azure)42:30🔒

Understanding ethical issues in data science50:20🔒
Data privacy and security considerations35:45🔒
Bias and fairness in machine learning models42:30🔒
Best practices for reproducible research42:30🔒
Responsible use of data and models42:30🔒

Overview of Data Science: Definition, importance, and applications25:30🔒
Data Science lifecycle: Data collection, data cleaning, analysis, modeling, and deployment30:45🔒
Key tools and technologies: Python, R, Jupyter Notebooks, SQL28:20🔒

Python basics: Syntax, data types, operators, control flow35:45🔒
Python libraries for data science: NumPy, pandas, Matplotlib25:20🔒
Introduction to R: Syntax, data types, basic data manipulation32:30🔒
Version control with Git: Basics of Git, repositories, branching, collaboration28:15🔒

Data types and structures: Arrays, lists, dictionaries, data frames35:20🔒
Data manipulation with pandas: Reading, cleaning, transforming, and merging datasets40:15🔒
Exploratory data analysis (EDA): Descriptive statistics, data visualization32:45🔒
Data visualization tools: Matplotlib, Seaborn, Plotly32:45🔒

Descriptive statistics: Mean, median, mode, variance, standard deviation38:30🔒
Probability basics: Probability theory, conditional probability, Bayes' theorem32:15🔒
Statistical inference: Sampling, confidence intervals, hypothesis testing32:15🔒
Correlation and regression: Understanding relationships between variables32:15🔒

Overview of machine learning: Types of learning (supervised, unsupervised, reinforcement)32:15🔒
Supervised learning: Regression (linear, logistic), classification (k-NN, SVM)28:40🔒
Unsupervised learning: Clustering (k-means, hierarchical), dimensionality reduction (PCA)35:20🔒
Model evaluation: Metrics for regression (MSE, RMSE) and classification (accuracy, precision, recall, F1 score)41:30🔒

Handling missing data: Imputation techniques, dealing with outliers45:30🔒
Feature selection and extraction: Selecting relevant features, creating new features38:25🔒
Data transformation: Normalization, standardization, encoding categorical variables42:15🔒
Time series data: Handling and analyzing time-dependent data42:15🔒

Advanced visualization techniques: Heatmaps, pair plots, interactive visualizations35:15🔒
Dashboard creation: Using tools like Tableau, Power BI, or Plotly Dash28:30🔒
Storytelling with data: Creating narratives with data, effective communication of insights32:45🔒
Visualization best practices: Design principles, color theory, and accessibility41:20🔒

Overview of big data: Definition, characteristics (Volume, Velocity, Variety)39:45🔒
Hadoop ecosystem: HDFS, MapReduce, Hadoop architecture42:20🔒
Introduction to Spark: Resilient Distributed Datasets (RDDs), DataFrames, Spark SQL48:35🔒
Working with big data: PySpark basics, integrating big data with data science workflows44:10🔒

Ensemble methods: Bagging, boosting, random forests, gradient boosting45:25🔒
Advanced regression techniques: Ridge regression, Lasso, ElasticNet38:15🔒
Support vector machines (SVM): Kernel tricks, hyperparameter tuning52:50🔒
Neural networks: Basics of artificial neural networks (ANNs), training and optimization41:40🔒

Introduction to deep learning: Neural networks, activation functions, loss functions42:30🔒
Deep learning frameworks: TensorFlow, Keras, PyTorch basics55:15🔒
Convolutional Neural Networks (CNNs): Architecture, applications in image processing38:40🔒
Recurrent Neural Networks (RNNs): LSTM, GRU, applications in sequence modeling38:40🔒

Overview of NLP: Text preprocessing, tokenization, stemming, lemmatization51:20🔒
Text representation techniques: Bag of Words, TF-IDF, word embeddings (Word2Vec, GloVe)43:35🔒
NLP models: Sentiment analysis, topic modeling, text classification39:15🔒
Advanced NLP: Transformer models, BERT, GPT, sequence-to-sequence models32:45🔒

Introduction to time series: Components of time series data, stationarity46:30🔒
Time series forecasting: ARIMA, SARIMA, Holt-Winters, exponential smoothing41:20🔒
Advanced time series models: Facebook Prophet, LSTM for time series forecasting38:45🔒
Anomaly detection in time series: Techniques for identifying outliers in time-dependent data38:45🔒
Price
From

$99.99

Courses Title
Web Development
Lessons
16 Videos
Language
English
Course Level
Beginner
Reviews
4.7(5.5k)
Quizzes
08
Duration
7 Weeks
Students
2.5k
Certifications
Yes
Pass Percentage
88%
Deadline
01 Jun, 2024
Instructor
Denial Lie
See All Reviews
FAQs

The duration is of 2 months for recorded & 3 months for live.

Yes at the end of the course completion you will get certificates.

ABCPanda team will arrange a doubt clearance session accordingly.

Yes for recorded sessions access duration 1 year. Live session access duration 2 years.

No our mentors will teach from basic. If you have experience, it would add an advantage.