Course Description
Data Science is one of the hottest fields of the 21st century. Data Science is a trending technology that gives useful information and insights by analyzing structured and unstructured data using scientific methods, processes, algorithms, and systems. The data science concept blends statistics, data analysis, machine learning, and related methods in order to understand and analyze the data. Data science with Python programming language has much scope in the IT industry and has a huge demand across the globe with honchos like Amazon, Google, Microsoft paying great salaries and perks to Data scientists, Data analytics. Learning Data Science with Python language will give you an extra edge in your career in the software industry.
Hachion’s Data Science with Python online training is prepared by the trained masters with all basic and advanced concepts of python programming language. This course provides you structured syllabus from scratch including basics of Python, data analysis, data scraping, data visualization, machine learning algorithms, etc. If you enjoy mathematics and statistics and have a piece of good practical business knowledge and the ability to present ideas in meaningful ways will definitely have a great impact to build a strong career in the Data Science field. The complete course will enhance your practical knowledge and programming skills by solving the assignments included within the Python Data Science tutorial. This course also provides hands-on experience through real-time projects.
Certification
ServiceNow Admin Certification
Servicenow Developer Certification
Who This Course is for
Anyone interested to learn the ServiceNow tool is welcome to join this course
Curriculum
- 26 Sections
- 184 Lessons
- 4 Weeks
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- Introduction to Data Science with Python14
- 2.1What is Analytics & Data Science?
- 2.2Common terms in Analytics
- 2.3Analytics vs. Data warehousing, OLAP, MIS Reporting
- 2.4Relevance in industry and need of the hour
- 2.5Types of problems and business objectives in various industries
- 2.6How leading companies are harnessing the power of analytics?
- 2.7Critical success drivers
- 2.8Overview of analytics tools & their popularity
- 2.9Analytics Methodology & problem-solving framework
- 2.10List of steps in Analytics projects
- 2.11Identify the most appropriate solution design for the given problem statement
- 2.12The project plan for Analytics project & key milestones based on effort estimates
- 2.13Build a Resource plan for an analytics project
- 2.14Why Python for Data science?
- Python: Essentials (Core)15
- 3.1Overview of Python – Starting with Python
- 3.2Introduction to installation of Python
- 3.3Introduction to Python Editors & IDE’s (Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
- 3.4Understand Jupyter notebook & Customize Settings
- 3.5Concept of Packages/Libraries – Important packages (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
- 3.6Installing & loading Packages & Name Spaces
- 3.7Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
- 3.8List and Dictionary Comprehensions
- 3.9Variable & Value Labels – Date & Time Values
- 3.10Basic Operations – Mathematical – string – date
- 3.11Reading and writing data
- 3.12Simple plotting
- 3.13Control flow & conditional statements
- 3.14Debugging & Code profiling
- 3.15How to create class and modules and how to call them?
- Scientific Distributions Used in Python For Data Science1
- Accessing/Importing and Exporting Data Using Python Modules5
- Data Manipulation – Cleansing – Munging Using Python Modules9
- 6.1Cleansing Data with Python
- 6.2Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
- 6.3Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays, etc)
- 6.4Python Built-in Functions (Text, numeric, date, utility functions)
- 6.5Python User Defined Functions
- 6.6Stripping out extraneous information
- 6.7Normalizing data
- 6.8Formatting data
- 6.9Important Python modules for data manipulation (Pandas, Numpy, re, math, string, DateTime etc)
- Data Analysis – Visualization Using Python6
- 7.1Introduction exploratory data analysis
- 7.2Descriptive statistics, Frequency Tables and summarization
- 7.3Univariate Analysis (Distribution of data & Graphical Analysis)
- 7.4Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
- 7.5Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
- 7.6Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
- Introduction to Statistics5
- 8.1Basic Statistics – Measures of Central Tendencies and Variance
- 8.2Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
- 8.3Inferential Statistics -Sampling – Concept of Hypothesis Testing
- 8.4Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chisquare
- 8.5Important modules for statistical methods: Numpy, Scipy, Pandas
- Introduction to Predictive Modeling5
- Data Exploration for Modeling5
- Data Preparation3
- Segmentation: Solving Segmentation Problems6
- 12.1Introduction to Segmentation
- 12.2Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
- 12.3Heuristic Segmentation Techniques (Value-Based, RFM Segmentation and Life Stage Segmentation)
- 12.4Behavioral Segmentation Techniques (K-Means Cluster Analysis)
- 12.5Cluster evaluation and profiling – Identify cluster characteristics
- 12.6Interpretation of results – Implementation on new data
- Linear Regression: Solving Regression Problems8
- 13.1Introduction – Applications
- 13.2Assumptions of Linear Regression
- 13.3Building Linear Regression Model
- 13.4Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis, etc)
- 13.5Assess the overall effectiveness of the model
- 13.6Validation of Models (Re-running Vs. Scoring)
- 13.7Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers, etc.)
- 13.8Interpretation of Results – Business Validation – Implementation on new data
- Ligistic Regression: Solving Classification Problems7
- 14.1Introduction – Applications
- 14.2Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
- 14.3Building Logistic Regression Model (Binary Logistic Model)
- 14.4Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve, etc)
- 14.5Validation of Logistic Regression Models (Re-running Vs. Scoring)
- 14.6Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
- 14.7Interpretation of Results – Business Validation – Implementation on new data
- Time Series Forecasting: Solving Forecasting Problems6
- 15.1Introduction – Applications
- 15.2Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
- 15.3Classification of Techniques(Pattern based – Pattern less)
- 15.4Basic Techniques – Averages, Smoothening, etc
- 15.5Advanced Techniques – AR Models, ARIMA, etc
- 15.6Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc
- Machine Learning -Predictive Modeling – Basics10
- 16.1Introduction to Machine Learning & Predictive Modeling
- 16.2Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
- 16.3Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
- 16.4Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
- 16.5Overfitting (Bias-Variance Tradeoff) & Performance Metrics
- 16.6Feature engineering & dimension reduction
- 16.7Concept of optimization & cost function
- 16.8Overview of gradient descent algorithm
- 16.9Overview of Cross-validation(Bootstrapping, K-Fold validation, etc)
- 16.10Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
- Unsupervised Learning: Segmentation7
- Supervised Learning: Decision Trees7
- 18.1Decision Trees – Introduction – Applications
- 18.2Types of Decision Tree Algorithms
- 18.3Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi-Square, Regression Trees
- 18.4Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
- 18.5Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
- 18.6Decision Trees – Validation
- 18.7Overfitting – Best Practices to avoid
- Supervised Learning: Ensemble Learning10
- 19.1Concept of Ensembling
- 19.2Manual Ensembling Vs. Automated Ensembling
- 19.3Methods of Ensembling (Stacking, Mixture of Experts)
- 19.4Bagging (Logic, Practical Applications)
- 19.5Random forest (Logic, Practical Applications)
- 19.6Boosting (Logic, Practical Applications)
- 19.7Boosting (Logic, Practical Applications)
- 19.8Ada Boost
- 19.9Gradient Boosting Machines (GBM)
- 19.10XGBoost
- Superviced Learning: Artificial Neural Networks (ANN)7
- 20.1Motivation for Neural Networks and Its Applications
- 20.2Perceptron and Single Layer Neural Network, and Hand Calculations
- 20.3Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
- 20.4Neural Networks for Regression
- 20.5Neural Networks for Classification
- 20.6Interpretation of Outputs and Fine tune the models with hyper parameters
- 20.7Validating ANN models
- Supervised Learning: Support Vector Machines6
- 21.1Motivation for Support Vector Machine & Applications
- 21.2Support Vector Regression
- 21.3Support vector classifier (Linear & Non-Linear)
- 21.4Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization, and Soft Constraints)
- 21.5Interpretation of Outputs and Fine tune the models with hyper parameters
- 21.6Validating SVM models
- Supervised Learning: KNN6
- Supervised Learning: Naive Bayes4
- Text Mining & Analytics12
- 24.1Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
- 24.2Finding patterns in text: text mining, text as a graph
- 24.3Natural Language processing (NLP)
- 24.4Text Analytics – Sentiment Analysis using Python
- 24.5Text Analytics – Word cloud analysis using Python
- 24.6Text Analytics – Segmentation using K-Means/Hierarchical Clustering
- 24.7Text Analytics – Classification (Spam/Not spam)
- 24.8Applications of Social Media Analytics
- 24.9Metrics(Measures Actions) in social media analytics
- 24.10Examples & Actionable Insights using Social Media Analytics
- 24.11Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
- 24.12Fine tuning the models using Hyper parameters, grid search, piping etc.
- R Programming4
- Statistics9
- Leading Topics7
