Data Science with Python Certification Training Course Get your data science career in motion with Python for the Data Science course. Prepzee offers a course that helps you gain proficiency in programming concepts of Python, such as file operations, data operations, and other Python libraries like Matplotlib, Pandas, and Numpy, which help in data manipulation, analysis, and visualization. The Data Science with Python course is in accordance with the standards of the industry and aids in gaining an understanding of Recommendation Systems, Machine Learning, and other specters of Data Science.
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36 Hrs Instructor-Led Training
40 Hrs Self-paced Videos
32 Hrs Project & Exercises
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A python is an essential tool for implementing data science applications.
The Python for Data Science training course begins with an introduction to cloud computing and Python. Understand Flow Control and Command-Line and discuss Python scrips on Windows/Unix. Learn about basic programming in Python and different applications and companies where Python is used.
Learn about object-oriented concepts in this module. Get a detailed understanding of standard libraries, Python functions and Parameters, Lambda functions-their features and syntax, and Modules used in Python. Learn how to handle errors and exceptions.
Get complete knowledge of what Time Series Analysis (TSA) is. The module gets into the detail of the components and significance of TSA. Understand how to use TSA in Python. Learn how to implement Dicky-Fuller Test and understand how to convert non-stationary data to stationary.
The curriculum of the course is designed by industry professionals keeping in mind the real-world requirements of the corporate world. Other than general guidelines, you will have help with preparing a resume, potential interview questions, mock interviews, and a reliable certification to go with it.
Data ScienceData VisualisationData AnalysisPythonMachine LearningPySparkAIMLOpsData WranglingSQLPrediction algorithmsNLP
Vishal Purohit
Product Manger at Icertis
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Great learning experience through the platform. The curriculum is updated and covers all the topics. The trainers are experts in their respective fields and follow more of a practical approach.
Abhishek Pareek
Technical Manager at Capgemini
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I was astonished by the way of training material and learning methodology they were using for their courses was amazing. Totally worth your money and time.
You are a fresher interested in learning programming.
You do not have any experience in coding but are looking to start a career in IT.
You have a basic knowledge of Python.
You wish to become a Data Scientist.
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1.1: Introduction to Cloud Computing
1.2: Overview of Python
1.3: The Companies using Python
1.4: Discuss Python Scripts on UNIX/Windows
1.5: Different Applications where Python is Used
1.6: Conditional Statements
1.7: Values, Types, Variables
1.8: Loops
1.9: Writing to the Screen
1.10: Operands and Expressions
1.11: Command Line Arguments
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2.1: Python files I/O Functions
2.2: Strings and their related operations
2.3: Lists and their related operations
2.4: Tuples and their related operations
2.5: Sets and their related operations
2.6: Dictionaries and their related operations
2.7: Numbers
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3.1: Functions
3.2: Function Parameters
3.3: Modules Used in Python
3.4: Module Search Path
3.5: Lambda Functions
3.6: Global Variables
3.7: Variable Scope and Returning Values
3.8: Object-Oriented Concepts
3.9: Standard Libraries
3.10: Package Installation Ways
3.11: The Import Statements
3.12: Errors and Exception Handling
3.13: Handling Multiple Exceptions
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4.1: Data Analysis
4.2: Pandas – data structures & index operations
4.3: Reading and Writing data from Excel/CSV formats into Pandas
4.4: NumPy – arrays
4.5: Operations on arrays
4.6: Reading and writing arrays on files
4.7: Indexing, slicing and iterating
4.8: Matplotlib library
4.9: Metadata for imported Datasets
4.10: Types of plots – bar graphs, pie charts, histograms
4.11: Contour plots
4.12: Markers, colors, fonts, and styling
4.13: Grids, axes, plots
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5.1: Basic Functionalities of a data object
5.2: Merging of Data objects
5.3: Concatenation of data objects
5.4: Types of Joins on data objects
5.5: Exploring a Dataset
5.6: Analysing a dataset
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6.1: Python Revision (NumPy, Pandas, scikit learn, matplotlib)
6.2: What is Machine Learning?
6.3: Machine Learning Categories
6.4: Machine Learning Process Flow
6.5: Machine Learning Use-Cases
6.6: Linear regression
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7.1: What are Classification and its use cases?
7.2: What is a Decision Tree?
7.3: Creating a Perfect Decision Tree
7.4: Algorithm for Decision Tree Induction
7.5: Confusion Matrix
7.6: What is Random Forest?
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8.1: Introduction to Dimensionality
8.2: Why Dimensionality Reduction
8.3: Factor Analysis
8.4: LDA
8.5: PCA
8.6: Scaling dimensional model
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9.1: Illustrate how the Support Vector Machine works.
9.2: Hyperparameter Optimisation
9.3: What is a Support Vector Machine?
9.4: Implementation of Support Vector Machine for Classification
9.5: What is Naïve Bayes?
9.6: How Naïve Bayes works?
9.7: Implementing Naïve Bayes Classifier
9.8: Grid Search vs. Random Search
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10.1: What is Hierarchical Clustering?
10.2: How does Hierarchical Clustering work?
10.3: What is K-means Clustering?
10.4: How does the K-means algorithm work?
10.5: How to do optimal clustering?
10.6: What is C-means Clustering?
10.7: What is Clustering & its Use Cases?
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11.1: What are Association Rules?
11.2: Association Rule Parameters
11.3: Calculating Association Rule Parameters
11.4: Recommendation Engines
11.5: How do Recommendation Engines work?
11.6: Content-Based Filtering
11.7: Collaborative Filtering
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12.1: What is Reinforcement Learning?
12.2: Why Reinforcement Learning?
12.3: Elements of Reinforcement Learning
12.4: Epsilon Greedy Algorithm
12.5: Exploration vs. Exploitation dilemma
12.6: Q – Learning
12.7: Q values and V values
12.8: Values
12.9: Markov Decision Process (MDP)
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13.1: What is Time Series Analysis?
13.2: Components of TSA
13.3: Importance of TSA
13.4: MA model
13.5: White Noise
13.6: AR model
13.7: ARIMA model
13.8: ARMA model
13.9: ACF & PACF
13.10: Stationarity
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14.1: What is Model Selection?
14.2: Need for Model Selection
14.3: What is Boosting?
14.4: Types of Boosting Algorithms
14.5: Adaptive Boosting
14.6: How do Boosting Algorithms work?
14.7: Cross–Validation
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15.1: What is Exploratory Data Analysis?
15.2: EDA Classification
15.3: EDA Techniques
15.4: Univariate Graphical EDA
15.5: Univariate Non-graphical EDA
15.6: Multivariate Graphical EDA
15.7: Multivariate Non-graphical EDA
15.8: Heat Maps
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16.1: Data Visualisation
16.2: Business Intelligence tools
16.3: VizQL Technology
16.4: Connect to data from the Database
16.5: Connect to data from File
16.6: Chart Operations
16.7: Basic Charts
16.8: Calculations
16.9: Combining Data
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17.1: Trend lines
17.2: Dashboards
17.3: Clustering
17.4: Reference lines
17.5: Forecasting
17.6: Using charts effectively
17.7: Geographic Maps
17.8: Publish to Tableau Online
17.9: Visual best practices
17.10: Story Points
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Our tutors are real business practitioners who hand-picked and created assignments and projects for you that you will encounter in real work.
Resume Parsing aids in collating the crucial information in the resumes into labels or cardinal categories. Essentially, a resume parser converts the processes of resumes into a format that only has vital information, thereby making the recruiter’s work less tiring and more manageable.
The project aims to extract faces from images and identify/classify a person's face in images and videos. The project helps you understand how to extract frames from a video, train using faces, and identify where the classified person is located in a video or an image.
The project aims at providing personal hotel recommendations based on the choices and needs of the user. The dataset for this project comprises: User details such as user name, age, location, and booking history are included.
Prepzee’s AWS Solutions Architect Certificate Holders work at 1000s of companies like
The trainer was very experienced. course content was awesome. Help and support from Prepzee is highly appreciable.
Venkatesh G was a great help mentoring me in Data science with Python training and helped me a lot during a live project for better understanding.
I found the best place Prepzee to upskill, the clear concept with the best live example helped me a lot with lots of tips given for further development.
I enrolled for the Data Science with python certification course and it was great. This platform is really great and it has helped me to grow in my career.
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