Duration: 1.5 months(Weekday) & 3 months(Weekend)
Data Science using Python
About Profound Data Science Classes?
According to the survey data scientist is the top ranking professional in the market. As a Data Scientist you need to understand the Business problem, gather data for the same and analyze by applying correct algorithms and techniques using current tools. A after running data through the Model you come out with the result of the exercise which include visualizing the result and communicates results to concern people in form of powepoint or some sort of report.
Need of Data Science
- Data Science is basically used to take better decision.
For example, whether to buy Self Driven Car or not. Current Data analysis shows it will root out more than 2 million deaths caused due to car accidents - Predictive Analysis
What Will Happen Next - Pattern Discovery
Is there any hidden information in the data. For example, to analyse patterns of sales that is, in which month it is increasing
What is Data Science?
- Asking the right Questions and Exploring the data
Basically, you need to know what problem you are solving that is asking the right question. After asking questions you will have data for that as input and you will perform certain action on it - Modeling the data using various algorithms
Suppose if you need to perform machine learning you have to decide which Model to use and which algorithms to use and then you need to train the model and so on. - Finally Communicating and Visualizing the results.
After running the data through the Model you come out with the result of the exercise which include visualizing the result and communicates results to concern people in form of powepoint or some sort of report. All the information that has been gathered
Why learn Data Science course at Profound Edutech?
- Data Science Course at profound is designed to train students and professional in the industry's most widely sought after skills, and make them job ready in the field of Data Science.
- We at profound Edutech provide you with an excellent platform to learn and explore the subjects from Industry experts.
- 100 % placement assurance for the trained candidates.
- Trainers with more than 10+ years experience
Eligibility: IT Professionals / Exposure to Information Technology
- Python Set up and Introduction
- Installation of softwares
- Introduction to Jupyter Notebook
- Introduction to Python
- Syntax
- Variables
- Built-in modules
- Python Operators
- Boolean's and Comparison
- Logical and Conditional Operators
- Data Types
- String, Integers, Float
- List
- Tuple
- Dictionary
- Set
- Mutability
- Flow Control
- If statement
- If...Else statement
- Elif statement
- For Loop
- While Loop
- Break and Continue statement
- range()
- Functions
- Built-in and User defined functions
- Arguments in functions
- Function with Loops
- Lambda Functions, List Comprehension
- File Handling
- Opening and Reading a File
- Opening and Writing Text to a Text File
- Appending to a Text File
- The With Statement
- Exception Handling
- Raising an Exception
- Handling Exception with Try and Except
- The Else Clause
- The Finally Clause
- Numpy
- Introduction
- Numpy 1D and 2D arrays
- Numpy Array indexing
- Numpy Operations
- Pandas
- Series
- DataFrames
- Conditional Selection and Indexing
- Missing Data
- GroupBy
- Merging, Joining and Concatenation
- Operations
- Data Input and Output
- Data Visualization
- Matplotlib
- Single Line and Multiline Plots
- Grid Axes and Labels
- Color Line Markers
- Seaborn
- Distribution Plots
- Category Plots
- Matrix Plots
- Grids
- Regression Plots
- Introduction to Plotly, Altair and ggplot
- Introduction to Statistics
- Mean, Median and Mode
- Histograms
- Variance
- Central Limit Theorem
- Normal Distribution and standard deviation
- Student’s T distribution
- Probability and Bayes Theorem
- Hypothesis Testing and Null Hypothesis
- p-values
- ‘Data’ and it’s relevance to industry
- Data sources, data generation
- Structured, semi-structured and unstructured data
- Qualitative and quantitative nature of data
- Basic data types
- Data storage methods
- Use cases of data in industry
- Data analysis
- Data, information, knowledge and wisdom
- Data domain, data description
- Data cleaning
- Data preparation
- Univariate and bivariate data analysis
- Extending data
- Using excel for data analysis
- Introduction to data modeling
- Need and objectives of modeling
- Pre-requisites
- Modeling, evaluation and deployment
- Categories of model
- Regression models
- Classification models
- Clustering models
- Meaning of supervised and unsupervised learning
- Introduction to Machine Learning
- Machine Learning Process
- Supervised Learning
- Regression
- Linear Regression
- Basic Concepts: OLS, Rsquared
- Data Visualization: Scatter
- Running the regression
- Multi-linear regression
- Logistic Regression
- Why take log
- Sigmoid function
- Confusion Matrix
- Multi-variable logistic regression
- Classification
- Naïve Bayes Classifiers
- Conditional Probability
- Bayes Rule
- K-NN Classification
- Decision Trees
- Root, Nodes and Leafs
- Gini impurity
- Random Forest
- Decision tree versus Random Forest
- Building random forest and estimate accuracy
- Support Vector Machines
- Vector, Dot Product
- Hyperplane
- Margin, Gama, Kernel
- Unsupervised Learning
- K-means clustering
- Elbow plot
- Pros and Cons of K-means clustering
- Hierarchical Clustering
- Neural Networks
- Introduction to Neural Networks
- The Linear Model
- Graphical Representation
- Objective Function
- Layers
- Activation functions
- Gradient Descent
- Back Propagation
- Convolution Neural Networks (CNN)
- Image Processing
- Convolution
- Max Pooling
- Flattening
- Full Connection
- Introduction to Recurrent Neural Network
- Natural Language Processing
- Convert Text to Symbols
- Vector Representations
- Sentiment Analysis
- Machine Reading
- Technical Assignments
- Technical Test
- Technical Interview
Project: Design, Development
Batch Schedule
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