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Data Science and Data Engineering using python- Day wise Syllabus |
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Day |
Topics |
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4hours/day |
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1 |
Introduction to Data Science, Importance of Python programming |
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Basics of Python, Basic of statistic using Python, Modules and packages in python |
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2 |
Exploratory Data Analysis (EDA), case study |
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3 |
Data Visualization, Skewness, Kurtosis, Probability Theory, case study |
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4 |
Normal Distribution, Z-score, Confidence Interval, Z-distribution, case study |
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5 |
T-distribution, Data Pre-processing, case study |
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6 |
Introduction to Machine learning, Importance of Machine learning in Data Science. Supervised |
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7 |
learning Algorithm: Naïve Bayes, case study |
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Supervised learning Algorithm: Random Forest, case study, Linear Regression, case study |
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8 |
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Unsupervised learning: K-means clustering, Hierarchal clustering and dendrogram, case study |
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9 |
Introduction to NLP , Data analysis using text mining, Text mining using NLP |
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10 |
Text mining case study, Introduction to WordCloud, Applications of wordcloud, case study |
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11 |
Time Series Analysis, Forecasting, case study |
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12 |
Dimension Reduction, Association Rule Mining, case study |
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13 |
Introduction to Data Engineering, Difference between Data Science and Data Engineer, |
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14 |
Different data sources, Procedure of data collection from different data sources, Introduction to |
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MYSQL |
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Basics of database, Configuring MYSQL server and client, Steps for database creation, |
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15 |
Database maintenance, Basics and advance database queries |
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Python programming for SQL, Integrating data engineering with data science. |
