TechPledge Certified Data Scientist (Python)

• Lectures (PDF)
•  Lab Manual (PDF)
• Recorded Video
• Xtream Lab / LOD Access
• Cloud Pass

Some of the responsibilities of a Python Data Scientist are:

- Collecting, cleaning, and analyzing large and complex datasets using Python libraries such as pandas, numpy, and scikit-learn.
- Developing and testing machine learning models and algorithms using Python frameworks such as TensorFlow, PyTorch, and Keras.
- Communicating and presenting the results and insights of data analysis and machine learning projects to stakeholders and clients using Python tools such as matplotlib, seaborn, and plotly.
- Collaborating with other data scientists, engineers, and domain experts on cross-functional teams using Python tools such as Jupyter Notebook, Git, and SQL.
- Keeping up to date with the latest developments and trends in data science and Python by reading research papers, blogs, and online courses.
 

- Introduction to Python programming

•   - Basic syntax, data types, variables, operators, expressions, statements, control structures, functions, modules, packages
•   - Working with common Python libraries such as NumPy, pandas, matplotlib, seaborn
•   - Writing and debugging Python code using Jupyter notebooks and other IDEs

- Data analysis and manipulation with pandas

•   - Loading, exploring, cleaning, transforming, and merging data from various sources
•   - Performing descriptive and inferential statistics on data
•   - Applying common data manipulation techniques such as filtering, grouping, aggregating, pivoting, reshaping, and joining data
•   - Visualizing data using pandas built-in plotting methods and other libraries

- Data visualization with matplotlib and seaborn

•   - Creating and customizing various types of plots such as line plots, bar plots, scatter plots, histograms, box plots, pie charts, etc.
•   - Enhancing plots with titles, labels, legends, annotations, colors, styles, etc.
•   - Using seaborn to create high-level statistical plots such as heatmaps, pairplots, jointplots, etc.
•   - Saving and exporting plots to different formats

- Machine learning with scikit-learn

•   - Understanding the basic concepts and terminology of machine learning
•   - Choosing and applying appropriate machine learning algorithms for different tasks such as regression, classification, clustering, dimensionality reduction, etc.
•   - Preparing data for machine learning such as scaling, encoding, splitting, etc.
•   - Evaluating and improving machine learning models using metrics such as accuracy, precision, recall, f1-score, confusion matrix, ROC curve, etc.
•   - Tuning model hyperparameters using grid search and cross-validation

- Deep learning with TensorFlow and Keras

•   - Getting started with TensorFlow and Keras
•   - Building and training neural networks using sequential and functional API
•   - Using various layers such as dense, convolutional, pooling, dropout, batch normalization, etc.
•   - Implementing common deep learning architectures such as CNNs, RNNs, LSTMs, GRUs for image recognition and natural language processing tasks
•   - Saving and loading models and weights
   

The course duration is 40 hours and the prerequisites are basic computer skills and familiarity with relational database concepts.
 

Note: For detailed syllabus and lab , please book a demo or email us at info@techpledges.com.