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CS3352 Foundation of data science (FODS)
Unit 11. Benefits and uses and process. of data science 2. Data Mining ,Data Warehousing3.Data analysis, building applicationsDon't share as screenshot Stuff SEctor
UNIT-21. data and variables typesDon't share as screenshot Stuff SEctor2.Normal Distributions and Standard (z) ScoresUNIT-31.Correlation,coefficient for quantitative data2. Regression , line, least squares,Standard error of estimate Don't share as screenshot Stuff SEctorUNIT-41. Data manipulation with Pandas**Don't share as screenshot Stuff SEctor2. Hierarchical indexing, aggregation, pivot tables**UNIT-51. Line plots ,Scatter plots,Sub plots2.Histograms, legends, colors
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**Very important questions are bolded and may be asked based on this topic
CS3352 Foundation of data science (FODS)
Don't share as screenshot -Stuff sector
**Very important questions are bolded and may be asked based on this topic
don't waste my hardwork and valuable time
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*These questions are expected for the exams This may or may not be asked for exams All the best.... from admin Santhosh
Thanks for your love and support guys keep supporting and share let the Engineers know about Us and leave a comment below for better improvements If there is any doubt feel free to ask me I will clear if I can or-else I will say some solutions ..get me through WhatsApp for instant updates ~$tuff$£ctorSYllabuSUNIT I INTRODUCTION
Data Science: Benefits and uses – facets of data - Data Science Process: Overview – Defining research
goals – Retrieving data – Data preparation - Exploratory Data analysis – build the model– presenting
findings and building applications - Data Mining - Data Warehousing – Basic Statistical descriptions of
Data
UNIT II DESCRIBING DATA
Types of Data - Types of Variables -Describing Data with Tables and Graphs –Describing Data with
Averages - Describing Variability - Normal Distributions and Standard (z) Scores
UNIT III DESCRIBING RELATIONSHIPS
Correlation –Scatter plots –correlation coefficient for quantitative data –computational formula for
correlation coefficient – Regression –regression line –least squares regression line – Standard error of
estimate – interpretation of r2 –multiple regression equations –regression towards the mean
UNIT IV PYTHON LIBRARIES FOR DATA WRANGLING
Basics of Numpy arrays –aggregations –computations on arrays –comparisons, masks, boolean logic
– fancy indexing – structured arrays – Data manipulation with Pandas – data indexing and selection –
operating on data – missing data – Hierarchical indexing – combining datasets – aggregation and
grouping – pivot tables
UNIT V DATA VISUALIZATION
Importing Matplotlib – Line plots – Scatter plots – visualizing errors – density and contour plots –
Histograms – legends – colors – subplots – text and annotation – customiz
UNIT I INTRODUCTION Data Science: Benefits and uses – facets of data - Data Science Process: Overview – Defining research goals – Retrieving data – Data preparation - Exploratory Data analysis – build the model– presenting findings and building applications - Data Mining - Data Warehousing – Basic Statistical descriptions of Data UNIT II DESCRIBING DATA Types of Data - Types of Variables -Describing Data with Tables and Graphs –Describing Data with Averages - Describing Variability - Normal Distributions and Standard (z) Scores UNIT III DESCRIBING RELATIONSHIPS Correlation –Scatter plots –correlation coefficient for quantitative data –computational formula for correlation coefficient – Regression –regression line –least squares regression line – Standard error of estimate – interpretation of r2 –multiple regression equations –regression towards the mean UNIT IV PYTHON LIBRARIES FOR DATA WRANGLING Basics of Numpy arrays –aggregations –computations on arrays –comparisons, masks, boolean logic – fancy indexing – structured arrays – Data manipulation with Pandas – data indexing and selection – operating on data – missing data – Hierarchical indexing – combining datasets – aggregation and grouping – pivot tables UNIT V DATA VISUALIZATION Importing Matplotlib – Line plots – Scatter plots – visualizing errors – density and contour plots – Histograms – legends – colors – subplots – text and annotation – customiz